diff --git a/Dashboard/__pycache__/models.cpython-310.pyc b/Dashboard/__pycache__/models.cpython-310.pyc index 23d5f35..12a6397 100644 Binary files a/Dashboard/__pycache__/models.cpython-310.pyc and b/Dashboard/__pycache__/models.cpython-310.pyc differ diff --git a/Dashboard/__pycache__/urls.cpython-310.pyc b/Dashboard/__pycache__/urls.cpython-310.pyc index fe32c45..b5cc431 100644 Binary files a/Dashboard/__pycache__/urls.cpython-310.pyc and b/Dashboard/__pycache__/urls.cpython-310.pyc differ diff --git a/Dashboard/__pycache__/views.cpython-310.pyc b/Dashboard/__pycache__/views.cpython-310.pyc index ea27bc9..4b43e1d 100644 Binary files a/Dashboard/__pycache__/views.cpython-310.pyc and b/Dashboard/__pycache__/views.cpython-310.pyc differ diff --git a/Dashboard/migrations/0006_ddosprediction_rensomware_auditprediction_and_more.py b/Dashboard/migrations/0006_ddosprediction_rensomware_auditprediction_and_more.py new file mode 100644 index 0000000..46be568 --- /dev/null +++ b/Dashboard/migrations/0006_ddosprediction_rensomware_auditprediction_and_more.py @@ -0,0 +1,46 @@ +# Generated by Django 5.1.3 on 2024-12-10 09:51 + +import django.db.models.deletion +from django.db import migrations, models + + +class Migration(migrations.Migration): + + dependencies = [ + ('Accounts', '0003_userprofile_company_name'), + ('Dashboard', '0005_restoredatabase'), + ('Device', '0007_devices_mac_address_devices_unique_id'), + ] + + operations = [ + migrations.CreateModel( + name='DdosPrediction', + fields=[ + ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), + ('file_path', models.FileField(upload_to='ddos_predictions/')), + ('uploaded_at', models.DateTimeField(auto_now_add=True)), + ('device', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='Device.devices')), + ('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='Accounts.userprofile')), + ], + ), + migrations.CreateModel( + name='Rensomware_AuditPrediction', + fields=[ + ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), + ('file_path', models.FileField(max_length=555, upload_to='ransomware_predictions/')), + ('uploaded_at', models.DateTimeField(auto_now_add=True)), + ('device', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='Device.devices')), + ('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='Accounts.userprofile')), + ], + ), + migrations.CreateModel( + name='Rensomware_TypePrediction', + fields=[ + ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), + ('file_path', models.FileField(max_length=555, upload_to='ransomware_predictions/')), + ('uploaded_at', models.DateTimeField(auto_now_add=True)), + ('device', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='Device.devices')), + ('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='Accounts.userprofile')), + ], + ), + ] diff --git a/Dashboard/migrations/__pycache__/0006_ddosprediction_rensomware_auditprediction_and_more.cpython-310.pyc b/Dashboard/migrations/__pycache__/0006_ddosprediction_rensomware_auditprediction_and_more.cpython-310.pyc new file mode 100644 index 0000000..f3e71e5 Binary files /dev/null and b/Dashboard/migrations/__pycache__/0006_ddosprediction_rensomware_auditprediction_and_more.cpython-310.pyc differ diff --git a/Dashboard/models.py b/Dashboard/models.py index 85496ac..58c6d31 100644 --- a/Dashboard/models.py +++ b/Dashboard/models.py @@ -1,5 +1,7 @@ from django.db import models +from Accounts .models import UserProfile +from Device.models import Devices # Create your models here. class Status(models.Model): number = models.CharField(max_length=15) @@ -23,4 +25,35 @@ class RestoreDatabase(models.Model): value = models.CharField(max_length=15, default="0") def __str__(self): - return f"Number (ID: {self.id}, Status: {self.value})" \ No newline at end of file + return f"Number (ID: {self.id}, Status: {self.value})" + + + +class DdosPrediction(models.Model): + device = models.ForeignKey(Devices, on_delete=models.CASCADE) + user = models.ForeignKey(UserProfile, on_delete=models.CASCADE) # Add this field to reference the user + file_path = models.FileField(upload_to='ddos_predictions/') + uploaded_at = models.DateTimeField(auto_now_add=True) + + def __str__(self): + return f"Prediction for {self.device.device_name} by {self.user.user.username} at {self.uploaded_at}" + +class Rensomware_TypePrediction(models.Model): + device = models.ForeignKey(Devices, on_delete=models.CASCADE) + user = models.ForeignKey(UserProfile, on_delete=models.CASCADE) # Add this field to reference the user + file_path = models.FileField(upload_to='ransomware_predictions/', max_length=555) + uploaded_at = models.DateTimeField(auto_now_add=True) + + def __str__(self): + return f"Prediction for {self.device.device_name} by {self.user.user.username} at {self.uploaded_at}" + +class Rensomware_AuditPrediction(models.Model): + device = models.ForeignKey(Devices, on_delete=models.CASCADE) + user = models.ForeignKey(UserProfile, on_delete=models.CASCADE) # Add this field to reference the user + file_path = models.FileField(upload_to='ransomware_predictions/',max_length=555) + uploaded_at = models.DateTimeField(auto_now_add=True) + + def __str__(self): + return f"Prediction for {self.device.device_name} by {self.user.user.username} at {self.uploaded_at}" + + \ No newline at end of file diff --git a/Dashboard/urls.py b/Dashboard/urls.py index 0995d89..6c8470e 100644 --- a/Dashboard/urls.py +++ b/Dashboard/urls.py @@ -47,7 +47,7 @@ urlpatterns = [ # DDoS path('generate/', generate_random_values, name='generate_random_values'), - path('fetch_ddos_value/', fetch_ddos_value, name='fetch_ddos_value'), + path('ddos/fetch_ddos_value/', fetch_ddos_value, name='fetch_ddos_value'), #shadow script path('status1/', views.get_number_status1, name='get_number_status1'), diff --git a/Dashboard/views.py b/Dashboard/views.py index ca84383..3c99126 100644 --- a/Dashboard/views.py +++ b/Dashboard/views.py @@ -12,6 +12,7 @@ from django.conf import settings import re import random from cryptography.fernet import Fernet +from django.template.loader import render_to_string from django.http import JsonResponse from Accounts.models import UserProfile from django.contrib.auth.models import User @@ -33,6 +34,7 @@ import csv from rest_framework.decorators import api_view from django.core.files.storage import default_storage from django.core.files.base import ContentFile +from Device .models import Devices import pandas as pd import time @@ -70,12 +72,119 @@ def navbar(request): #=========================================================================================== @never_cache @login_required(login_url='login') +# def ddos(request): + +# file_path = 'media/ddos_predictions/predictions.csv' +# data = pd.read_csv(file_path) + +# # Create a mapping for protocol names to their short forms +# protocol_mapping = { +# "Protocol_ICMP": "ICMP", +# "Protocol_TCP": "TCP", +# "Protocol_UDP": "UDP", +# "Protocol_HTTP": "HTTP", +# "Protocol_HTTPS": "HTTPS", +# "Protocol_SSH": "SSH", +# "Protocol_DHCP": "DHCP", +# "Protocol_FTP": "FTP", +# "Protocol_SMTP": "SMTP", +# "Protocol_POP3": "POP3", +# "Protocol_IMAP": "IMAP", +# "Protocol_DNS": "DNS" +# } + + + +# ddos_columns = ['pktcount', 'byteperflow', 'tot_kbps', 'rx_kbps', 'flows', 'bytecount', 'tot_dur'] +# ddos_sums = {col: int(data[col].sum()) for col in ddos_columns} +# ddos_sums['byteperflow'] /= 15 +# ddos_sums['tot_kbps'] /= 15 + +# src_ip_counts = data['src_ip'].value_counts() +# src_ip_dict = src_ip_counts.to_dict() + + +# dest_ip_counts = data['dst_ip'].value_counts() +# dest_ip_dict = dest_ip_counts.to_dict() + + +# protocol_columns = data.columns[7:19] +# protocol_counts = {} +# for protocol in protocol_columns: +# short_form = protocol_mapping.get(protocol, protocol) # Default to the original name if not found +# protocol_counts[short_form] = int((data[protocol] == 1).sum()) +# print(protocol_counts) + +# filtered_data = data[data['probability'] > 0.9] +# src_ip_counts2 = filtered_data['src_ip'].value_counts() +# src_ip_dict2 = src_ip_counts2.to_dict() + + + + + + + + + + +# return render(request, 'ddos/ddos.html',{'ddos_sums': ddos_sums,'src_ip_dict' : src_ip_dict , 'dest_ip_dict' : dest_ip_dict , 'protocol_counts' : protocol_counts,'src_ip_dict2' : src_ip_dict2}) +@login_required(login_url='login') def ddos(request): + print("Inside the ddos view...") - file_path = 'media/ddos_predictions/predictions.csv' - data = pd.read_csv(file_path) + # Attempt to get the device_id from request headers + device_id = request.GET.get('device_id', None) + print(f"Device ID from headers: {device_id}") - # Create a mapping for protocol names to their short forms + # If device_id is not provided in headers, fetch the latest device for the logged-in user + if not device_id: + print("No device ID provided in headers. Fetching the latest device for the logged-in user.") + recent_device = Devices.objects.filter(used_by__user=request.user).order_by('-id').first() + if recent_device: + device_id = recent_device.id + print(f"Fetched recent device ID: {device_id}") + else: + print("No devices found for the logged-in user.") + return JsonResponse({'error': 'No devices found for the logged-in user'}, status=404) + + # Convert device_id to integer + try: + device_id = int(device_id) + print(f"Using device ID: {device_id}") + except ValueError: + return JsonResponse({'error': 'Invalid device ID'}, status=400) + + # Retrieve the device and its pod + try: + device = Devices.objects.get(id=device_id) + device_pod = device.pod + print(f"Device Pod: {device_pod}") + except Devices.DoesNotExist: + return JsonResponse({'error': f"Device with ID {device_id} not found"}, status=404) + + # Construct the file path for the device's DDoS prediction CSV file + file_path = os.path.join('media', 'ddos_predictions', str(device_id), 'predictions.csv') + # file_path = os.path.join('media', 'ddos_predictions', 'predictions.csv') + print(f"Constructed file path: {file_path}") + + # Check if the file exists + if not os.path.exists(file_path): + print(f"File not found at path: {file_path}") + return JsonResponse({'error': f"File not found for device ID {device_id}"}, status=404) + + # Attempt to read the CSV file + try: + data = pd.read_csv(file_path) + print(f"Data loaded successfully. First rows:\n{data.head()}") + except pd.errors.EmptyDataError: + print(f"CSV file is empty: {file_path}") + return JsonResponse({'error': 'CSV file is empty'}, status=400) + except Exception as e: + print(f"Unexpected error reading CSV: {e}") + return JsonResponse({'error': 'Error reading the CSV file'}, status=500) + + # Process the CSV data protocol_mapping = { "Protocol_ICMP": "ICMP", "Protocol_TCP": "TCP", @@ -91,8 +200,6 @@ def ddos(request): "Protocol_DNS": "DNS" } - - ddos_columns = ['pktcount', 'byteperflow', 'tot_kbps', 'rx_kbps', 'flows', 'bytecount', 'tot_dur'] ddos_sums = {col: int(data[col].sum()) for col in ddos_columns} ddos_sums['byteperflow'] /= 15 @@ -101,32 +208,37 @@ def ddos(request): src_ip_counts = data['src_ip'].value_counts() src_ip_dict = src_ip_counts.to_dict() - dest_ip_counts = data['dst_ip'].value_counts() dest_ip_dict = dest_ip_counts.to_dict() - protocol_columns = data.columns[7:19] protocol_counts = {} for protocol in protocol_columns: - short_form = protocol_mapping.get(protocol, protocol) # Default to the original name if not found + short_form = protocol_mapping.get(protocol, protocol) protocol_counts[short_form] = int((data[protocol] == 1).sum()) - print(protocol_counts) + # Filter data where the probability is above 0.9 filtered_data = data[data['probability'] > 0.9] src_ip_counts2 = filtered_data['src_ip'].value_counts() src_ip_dict2 = src_ip_counts2.to_dict() + # Return the response with the DDoS data and device pod + try: + rendered_html = render_to_string('ddos/ddos.html', { + 'ddos_sums': ddos_sums, + 'src_ip_dict': src_ip_dict, + 'dest_ip_dict': dest_ip_dict, + 'protocol_counts': protocol_counts, + 'src_ip_dict2': src_ip_dict2, + 'device_pod': device_pod, # Include device pod in the context + }) - + return HttpResponse(rendered_html, status=200) + except Exception as e: + print(f"Error processing data: {e}") + return JsonResponse({'error': 'Error processing DDoS data'}, status=500) - - - - - - return render(request, 'ddos/ddos.html',{'ddos_sums': ddos_sums,'src_ip_dict' : src_ip_dict , 'dest_ip_dict' : dest_ip_dict , 'protocol_counts' : protocol_counts,'src_ip_dict2' : src_ip_dict2}) #================================================================================ @@ -362,55 +474,158 @@ def processes_log(request): def dma(request): return render(request, 'dma/dma.html') -def get_combined_files(): +# def get_combined_files(): - df1 = pd.read_csv('media/malware_predictions/bytes_predictions_KNeighborsClassifier.csv') - df2 = pd.read_csv('media/malware_predictions/bytes_predictions_RandomForestClassifier.csv') - df3 = pd.read_csv('media/malware_predictions/latest_malware_bytes_predictions_SGD.csv') - df4 = pd.read_csv('media/malware_predictions/latest_malware_bytes_predictions_XGB.csv') +# df1 = pd.read_csv('media/malware_predictions/bytes_predictions_KNeighborsClassifier.csv') +# df2 = pd.read_csv('media/malware_predictions/bytes_predictions_RandomForestClassifier.csv') +# df3 = pd.read_csv('media/malware_predictions/latest_malware_bytes_predictions_SGD.csv') +# df4 = pd.read_csv('media/malware_predictions/latest_malware_bytes_predictions_XGB.csv') - # df1 = pd.read_csv('media/temp/bytes_predictions_KNeighborsClassifier.csv') - # df2 = pd.read_csv('media/temp/bytes_predictions_RandomForestClassifier.csv') - # df3 = pd.read_csv('media/temp/bytes_predictions_SGDClassifier.csv') - # df4 = pd.read_csv('media/temp/bytes_predictions_XGBooster.csv') +# # df1 = pd.read_csv('media/temp/bytes_predictions_KNeighborsClassifier.csv') +# # df2 = pd.read_csv('media/temp/bytes_predictions_RandomForestClassifier.csv') +# # df3 = pd.read_csv('media/temp/bytes_predictions_SGDClassifier.csv') +# # df4 = pd.read_csv('media/temp/bytes_predictions_XGBooster.csv') - # Step 2: Create a new DataFrame to hold combined results - combined_data1 = pd.DataFrame() +# # Step 2: Create a new DataFrame to hold combined results +# combined_data1 = pd.DataFrame() - # Step 3: Combine predictions - combined_data1['File'] = df1['File'] # Assuming all files are the same - combined_data1['Predicted Class'] = df1['Predicted Class'] # Placeholder - combined_data1['Prediction Probability'] = 0.0 # Initialize probability column - max_length = max(len(df1), len(df2), len(df3), len(df4)) - # Step 4: Loop through each row and calculate the highest probability and average - # for i in range(len(df1)): - # # Get probabilities from all models - # probs = [ - # df1['Prediction Probability'][i], - # df2['Prediction Probability'][i], - # df3['Prediction Probability'][i], - # df4['Prediction Probability'][i], - # ] +# # Step 3: Combine predictions +# combined_data1['File'] = df1['File'] # Assuming all files are the same +# combined_data1['Predicted Class'] = df1['Predicted Class'] # Placeholder +# combined_data1['Prediction Probability'] = 0.0 # Initialize probability column +# max_length = max(len(df1), len(df2), len(df3), len(df4)) +# # Step 4: Loop through each row and calculate the highest probability and average +# # for i in range(len(df1)): +# # # Get probabilities from all models +# # probs = [ +# # df1['Prediction Probability'][i], +# # df2['Prediction Probability'][i], +# # df3['Prediction Probability'][i], +# # df4['Prediction Probability'][i], +# # ] - # # Get predicted classes - # classes = [ - # df1['Predicted Class'][i], - # df2['Predicted Class'][i], - # df3['Predicted Class'][i], - # df4['Predicted Class'][i], - # ] +# # # Get predicted classes +# # classes = [ +# # df1['Predicted Class'][i], +# # df2['Predicted Class'][i], +# # df3['Predicted Class'][i], +# # df4['Predicted Class'][i], +# # ] - # # Find the index of the highest probability - # max_index = probs.index(max(probs)) +# # # Find the index of the highest probability +# # max_index = probs.index(max(probs)) - # # Set the highest predicted class - # combined_data1.at[i, 'Predicted Class'] = classes[max_index] +# # # Set the highest predicted class +# # combined_data1.at[i, 'Predicted Class'] = classes[max_index] - # # Calculate the average probability - # combined_data1.at[i, 'Prediction Probability'] = sum(probs) / len(probs) +# # # Calculate the average probability +# # combined_data1.at[i, 'Prediction Probability'] = sum(probs) / len(probs) - for i in range(max_length): +# for i in range(max_length): +# probs, classes = [], [] + +# for df in [df1, df2, df3, df4]: +# try: +# probs.append(df['Prediction Probability'].iloc[i]) +# classes.append(df['Predicted Class'].iloc[i]) +# except IndexError: +# # Skip if the row does not exist in this DataFrame +# pass + +# if probs and classes: +# max_index = probs.index(max(probs)) +# combined_data1.at[i, 'Predicted Class'] = classes[max_index] +# combined_data1.at[i, 'Prediction Probability'] = sum(probs) / len(probs) +# df5 = pd.read_csv('media/malware_predictions/latest_malware_ASM_predictions_KNeighbours.csv') +# df6 = pd.read_csv('media/malware_predictions/latest_malware_ASM_predictions_LogisticRegression.csv') +# df7 = pd.read_csv('media/malware_predictions/latest_malware_ASM_predictions_RandomForest.csv') +# df8 = pd.read_csv('media/malware_predictions/latest_malware_ASM_predictions_XGB.csv') + +# combined_data2 = pd.DataFrame() + +# # Step 3: Combine predictions +# combined_data2['File'] = df5['File'] # Assuming all files are the same +# combined_data2['Predicted Class'] = df5['Predicted Class'] # Placeholder +# combined_data2['Prediction Probability'] = 0.0 # Initialize probability column + +# # Step 4: Loop through each row and calculate the highest probability and average +# for i in range(len(df5)): +# # Get probabilities from all models +# probs = [ +# df5['Prediction Probability'][i], +# df6['Prediction Probability'][i], +# df7['Prediction Probability'][i], +# df8['Prediction Probability'][i], +# ] + +# # Get predicted classes +# classes = [ +# df5['Predicted Class'][i], +# df6['Predicted Class'][i], +# df7['Predicted Class'][i], +# df8['Predicted Class'][i], +# ] + +# # Find the index of the highest probability +# max_index = probs.index(max(probs)) + +# # Set the highest predicted class +# combined_data2.at[i, 'Predicted Class'] = classes[max_index] + +# # Calculate the average probability +# # combined_data2.at[i, 'Prediction Probability'] = sum(probs) / len(probs) +# combined_data2.at[i,'Prediction Probability'] = probs[max_index] + + +# combined_data = pd.concat([combined_data1, combined_data2], ignore_index=True) + +# return combined_data + +def get_combined_files(device_id): + try: + + df1 = pd.read_csv(f'media/malware_predictions/{device_id}/bytes_predictions_KNeighborsClassifier.csv') + df2 = pd.read_csv(f'media/malware_predictions/{device_id}/bytes_predictions_RandomForestClassifier.csv') + df3 = pd.read_csv(f'media/malware_predictions/{device_id}/bytes_predictions_SGDClassifier.csv') + df4 = pd.read_csv(f'media/malware_predictions/{device_id}/bytes_predictions_XGBClassifier.csv') + + # Step 2: Create a new DataFrame to hold combined results + combined_data1 = pd.DataFrame() + + # Step 3: Combine predictions + combined_data1['File'] = df1['File'] # Assuming all files are the same + combined_data1['Predicted Class'] = df1['Predicted Class'] # Placeholder + combined_data1['Prediction Probability'] = 0.0 # Initialize probability column + max_length = max(len(df1), len(df2), len(df3), len(df4)) + # Step 4: Loop through each row and calculate the highest probability and average + # for i in range(len(df1)): + # # Get probabilities from all models + # probs = [ + # df1['Prediction Probability'][i], + # df2['Prediction Probability'][i], + # df3['Prediction Probability'][i], + # df4['Prediction Probability'][i], + # ] + + # # Get predicted classes + # classes = [ + # df1['Predicted Class'][i], + # df2['Predicted Class'][i], + # df3['Predicted Class'][i], + # df4['Predicted Class'][i], + # ] + + # # Find the index of the highest probability + # max_index = probs.index(max(probs)) + + # # Set the highest predicted class + # combined_data1.at[i, 'Predicted Class'] = classes[max_index] + + # # Calculate the average probability + # combined_data1.at[i, 'Prediction Probability'] = sum(probs) / len(probs) + + for i in range(max_length): probs, classes = [], [] for df in [df1, df2, df3, df4]: @@ -425,57 +640,100 @@ def get_combined_files(): max_index = probs.index(max(probs)) combined_data1.at[i, 'Predicted Class'] = classes[max_index] combined_data1.at[i, 'Prediction Probability'] = sum(probs) / len(probs) - df5 = pd.read_csv('media/malware_predictions/latest_malware_ASM_predictions_KNeighbours.csv') - df6 = pd.read_csv('media/malware_predictions/latest_malware_ASM_predictions_LogisticRegression.csv') - df7 = pd.read_csv('media/malware_predictions/latest_malware_ASM_predictions_RandomForest.csv') - df8 = pd.read_csv('media/malware_predictions/latest_malware_ASM_predictions_XGB.csv') - combined_data2 = pd.DataFrame() + df5 = pd.read_csv(f'media/malware_predictions/{device_id}/asm_prediction_KNeighborsClassifier.csv') + df6 = pd.read_csv(f'media/malware_predictions/{device_id}/asm_prediction_LogisticRegression.csv') + df7 = pd.read_csv(f'media/malware_predictions/{device_id}/asm_prediction_RandomForestClassifier.csv') + df8 = pd.read_csv(f'media/malware_predictions/{device_id}/asm_prediction_XGBClassifier.csv') - # Step 3: Combine predictions - combined_data2['File'] = df5['File'] # Assuming all files are the same - combined_data2['Predicted Class'] = df5['Predicted Class'] # Placeholder - combined_data2['Prediction Probability'] = 0.0 # Initialize probability column - - # Step 4: Loop through each row and calculate the highest probability and average - for i in range(len(df5)): - # Get probabilities from all models - probs = [ - df5['Prediction Probability'][i], - df6['Prediction Probability'][i], - df7['Prediction Probability'][i], - df8['Prediction Probability'][i], - ] - # Get predicted classes - classes = [ - df5['Predicted Class'][i], - df6['Predicted Class'][i], - df7['Predicted Class'][i], - df8['Predicted Class'][i], - ] - - # Find the index of the highest probability - max_index = probs.index(max(probs)) - - # Set the highest predicted class - combined_data2.at[i, 'Predicted Class'] = classes[max_index] - - # Calculate the average probability - # combined_data2.at[i, 'Prediction Probability'] = sum(probs) / len(probs) - combined_data2.at[i,'Prediction Probability'] = probs[max_index] - - combined_data = pd.concat([combined_data1, combined_data2], ignore_index=True) + combined_data2 = pd.DataFrame() - return combined_data + # Step 3: Combine predictions + combined_data2['File'] = df5['File'] # Assuming all files are the same + combined_data2['Predicted Class'] = df5['Predicted Class'] # Placeholder + combined_data2['Prediction Probability'] = 0.0 # Initialize probability column + max_length = max(len(df5), len(df6), len(df7), len(df8)) + + # Step 4: Loop through each row and calculate the highest probability and average + # for i in range(len(df5)): + # # Get probabilities from all models + # probs = [ + # df5['Prediction Probability'][i], + # df6['Prediction Probability'][i], + # df7['Prediction Probability'][i], + # df8['Prediction Probability'][i], + # ] + + # # Get predicted classes + # classes = [ + # df5['Predicted Class'][i], + # df6['Predicted Class'][i], + # df7['Predicted Class'][i], + # df8['Predicted Class'][i], + # ] + + # # Find the index of the highest probability + # max_index = probs.index(max(probs)) + + # # Set the highest predicted class + # combined_data2.at[i, 'Predicted Class'] = classes[max_index] + + # # Calculate the average probability + # # combined_data2.at[i, 'Prediction Probability'] = sum(probs) / len(probs) + # combined_data2.at[i,'Prediction Probability'] = probs[max_index] + for i in range(max_length): + probs, classes = [], [] + + for df in [df5, df6, df7, df8]: + try: + probs.append(df['Prediction Probability'].iloc[i]) + classes.append(df['Predicted Class'].iloc[i]) + except IndexError: + # Skip if the row does not exist in this DataFrame + pass + + if probs and classes: + max_index = probs.index(max(probs)) + combined_data1.at[i, 'Predicted Class'] = classes[max_index] + combined_data1.at[i, 'Prediction Probability'] = sum(probs) / len(probs) + + combined_data = pd.concat([combined_data1, combined_data2], ignore_index=True) + + return combined_data + except FileNotFoundError as e: + # Handle missing files: log the error and return an empty DataFrame + print(f"File not found: {e}") + return pd.DataFrame() @login_required(login_url='login') @never_cache -def malware(request): - combined_data = get_combined_files() +def malware(request): + print("Inside the ddos view...") + + device_id = request.GET.get('device_id', None) + print(f"Device ID from headers: {device_id}") + + if not device_id: + print("No device ID provided in headers. Fetching the latest device for the logged-in user.") + recent_device = Devices.objects.filter(used_by__user=request.user).order_by('-id').first() + if recent_device: + device_id = recent_device.id + print(f"Fetched recent device ID: {device_id}") + else: + print("No devices found for the logged-in user.") + + # Fetch combined data + combined_data = get_combined_files(device_id) + + # If the data is empty, show a message + if combined_data.empty: + message = "Data is still being captured. Please try again later." + return render(request, 'malware/malware.html', {'message': message}) + class_names = { 1: "Ramnit", 2: "Lollipop", @@ -487,25 +745,17 @@ def malware(request): 8: "Obfuscator.ACY", 9: "Gatak" } - high_probability_files = combined_data[combined_data['Prediction Probability'] >= 0.9] files_list = high_probability_files['File'].tolist() - files70_90 = combined_data[(combined_data['Prediction Probability'] >= 0.7) & (combined_data['Prediction Probability'] < 0.9)] frequency = files70_90['Predicted Class'].value_counts().sort_index() complete_index = pd.Index(range(10)) frequency = frequency.reindex(complete_index, fill_value=0) - print(frequency,'in the frequency') - # if frequency: - # print("Check_malware_frequency") - all_frequency = combined_data['Predicted Class'].value_counts().reindex(range(1, 10), fill_value=0).sort_index() frequency_with_names = all_frequency.rename(class_names) - print(frequency_with_names,'with name') - avg_probability = combined_data.groupby('Predicted Class')['Prediction Probability'].mean().reset_index() all_classes = pd.DataFrame({'Predicted Class': range(1, 10)}) @@ -513,19 +763,77 @@ def malware(request): avg_probability['Prediction Probability'].fillna(0, inplace=True) avg_probability['Class Name'] = avg_probability['Predicted Class'].map(class_names) average_probability_dict = dict(zip(avg_probability['Class Name'], avg_probability['Prediction Probability'])) - print(average_probability_dict,"avg is here ") - + file_path = os.path.join(settings.MEDIA_ROOT, 'logs', 'logs.txt') data = None try: - with open(file_path, 'r') as file: - data = file.readlines()[::-1] # Reverse lines for latest logs + with open(file_path, 'r') as file: + data = file.readlines()[::-1] # Reverse lines for latest logs except: - pass + pass + + return render(request, 'malware/malware.html', { + 'files_list': files_list, + 'frequency': frequency.to_dict(), + 'class_frequency': frequency_with_names.to_dict(), + 'average': average_probability_dict, + "logs": data, + 'message': None # Clear message if data is available + }) + +# def malware(request): +# combined_data = get_combined_files() + +# class_names = { +# 1: "Ramnit", +# 2: "Lollipop", +# 3: "Kelihos_ver3", +# 4: "Vundo", +# 5: "Simda", +# 6: "Tracur", +# 7: "Kelihos_ver1", +# 8: "Obfuscator.ACY", +# 9: "Gatak" +# } + + +# high_probability_files = combined_data[combined_data['Prediction Probability'] >= 0.9] +# files_list = high_probability_files['File'].tolist() + + +# files70_90 = combined_data[(combined_data['Prediction Probability'] >= 0.7) & (combined_data['Prediction Probability'] < 0.9)] +# frequency = files70_90['Predicted Class'].value_counts().sort_index() +# complete_index = pd.Index(range(10)) +# frequency = frequency.reindex(complete_index, fill_value=0) +# print(frequency,'in the frequency') +# # if frequency: +# # print("Check_malware_frequency") + + +# all_frequency = combined_data['Predicted Class'].value_counts().reindex(range(1, 10), fill_value=0).sort_index() +# frequency_with_names = all_frequency.rename(class_names) +# print(frequency_with_names,'with name') + + +# avg_probability = combined_data.groupby('Predicted Class')['Prediction Probability'].mean().reset_index() +# all_classes = pd.DataFrame({'Predicted Class': range(1, 10)}) +# avg_probability = pd.merge(all_classes, avg_probability, on='Predicted Class', how='left') +# avg_probability['Prediction Probability'].fillna(0, inplace=True) +# avg_probability['Class Name'] = avg_probability['Predicted Class'].map(class_names) +# average_probability_dict = dict(zip(avg_probability['Class Name'], avg_probability['Prediction Probability'])) +# print(average_probability_dict,"avg is here ") + +# file_path = os.path.join(settings.MEDIA_ROOT, 'logs', 'logs.txt') +# data = None +# try: +# with open(file_path, 'r') as file: +# data = file.readlines()[::-1] # Reverse lines for latest logs +# except: +# pass - return render(request, 'malware/malware.html', {'files_list': files_list , 'frequency' : frequency.to_dict() , 'class_frequency' : frequency_with_names.to_dict() , 'average' : average_probability_dict ,"logs":data}) +# return render(request, 'malware/malware.html', {'files_list': files_list , 'frequency' : frequency.to_dict() , 'class_frequency' : frequency_with_names.to_dict() , 'average' : average_probability_dict ,"logs":data}) @never_cache def bye_asm_log(request): @@ -559,11 +867,63 @@ def bye_asm_log(request): +# @login_required(login_url='login') +# @never_cache +# def ransomware(request): + +# file_path = 'media/logs/usage_log.txt' +# cpu_data = [] +# memory_data = [] + +# # Read data from the log file +# if os.path.exists(file_path): +# with open(file_path, 'r') as f: +# lines = f.readlines() + +# # Extract the last 5 entries +# lines = lines[-5:] + +# for line in lines: +# # Parse CPU and memory usage from each line +# parts = line.strip().split(",") +# cpu_usage = parts[0] +# memory_usage = parts[1] +# cpu_data.append(cpu_usage) +# memory_data.append(memory_usage) + + + + + + +# csv_file_path = 'media/ransomware_predictions/latest_ransomware_type.csv' # Replace with your actual CSV file path +# df = pd.read_csv(csv_file_path) +# mapping_file_path = 'media/ransomware_predictions/mapping_win.txt' +# mapping_df = pd.read_csv(mapping_file_path, header=None, names=['predicted_class', 'class_name']) +# class_mapping = dict(zip(mapping_df['predicted_class'], mapping_df['class_name'])) +# df['class_name'] = df['predicted_class'].map(class_mapping) +# class_frequency = df['class_name'].value_counts() +# all_classes_df = pd.DataFrame({'class_name': mapping_df['class_name']}) +# all_classes_df['frequency'] = all_classes_df['class_name'].map(class_frequency).fillna(0).astype(int) +# class_frequency_dict = dict(zip(all_classes_df['class_name'], all_classes_df['frequency'])) + +# yes_no_path = 'media/ransomware_predictions/ransomware.csv' + +# # Reading the CSV file into a DataFrame +# yes_no = pd.read_csv(yes_no_path) + +# # # Extracting the value of 'Predicted Label' +# flag =yes_no[yes_no.columns[-1]].iloc[0] +# time = yes_no[yes_no.columns[-2]].iloc[0] + + + + +# return render(request, 'ransomware/ransomware.html' , context={ 'type' : class_frequency_dict, 'cpu' : json.dumps(cpu_data) , 'memory' : json.dumps(memory_data) , 'flag' : flag,'time' : time}) @login_required(login_url='login') @never_cache def ransomware(request): - - file_path = 'media/logs/usage_log.txt' + file_path = os.path.join('media', 'logs', 'usage_log.txt') cpu_data = [] memory_data = [] @@ -578,40 +938,118 @@ def ransomware(request): for line in lines: # Parse CPU and memory usage from each line parts = line.strip().split(",") - cpu_usage = parts[0] - memory_usage = parts[1] - cpu_data.append(cpu_usage) - memory_data.append(memory_usage) + if len(parts) >= 2: + cpu_usage = parts[0] + memory_usage = parts[1] + cpu_data.append(cpu_usage) + memory_data.append(memory_usage) + else: + print(f"Skipping malformed line: {line}") + else: + print(f"Usage log file not found at path: {file_path}") - + device_id = request.GET.get('device_id', None) + # device_id=53 + print(f"Device ID from headers: {device_id}") + # If no device_id is found in the request, get the latest device for the logged-in user + if not device_id: + print("No device ID found. Fetching the latest device for the logged-in user.") + recent_device = Devices.objects.filter(used_by__user=request.user).order_by('-id').first() + if recent_device: + device_id = recent_device.id # Use the actual device ID from the database + print(f"Fetched recent device ID: {device_id}") + else: + print("No devices found for the logged-in user.") + return JsonResponse({'error': 'No devices found for the logged-in user'}, status=404) + # # Construct file paths correctly using os.path.join + csv_file_path = os.path.join('media', 'ransomware_predictions', str(device_id), 'latest_ransomware_type.csv') + # csv_file_path = os.path.join('media', 'ransomware_predictions','latest_ransomware_type.csv') + mapping_file_path = os.path.join('media', 'ransomware_predictions', 'mapping_win.txt') + yes_no_path = os.path.join('media', 'ransomware_predictions', 'ransomware.csv') - csv_file_path = 'media/ransomware_predictions/latest_ransomware_type.csv' # Replace with your actual CSV file path - df = pd.read_csv(csv_file_path) - mapping_file_path = 'media/ransomware_predictions/mapping_win.txt' - mapping_df = pd.read_csv(mapping_file_path, header=None, names=['predicted_class', 'class_name']) - class_mapping = dict(zip(mapping_df['predicted_class'], mapping_df['class_name'])) - df['class_name'] = df['predicted_class'].map(class_mapping) - class_frequency = df['class_name'].value_counts() - all_classes_df = pd.DataFrame({'class_name': mapping_df['class_name']}) - all_classes_df['frequency'] = all_classes_df['class_name'].map(class_frequency).fillna(0).astype(int) - class_frequency_dict = dict(zip(all_classes_df['class_name'], all_classes_df['frequency'])) + # Debugging: Print the file paths + print(f"CSV file path: {csv_file_path}") + print(f"Mapping file path: {mapping_file_path}") + print(f"Yes/No file path: {yes_no_path}") - yes_no_path = 'media/ransomware_predictions/ransomware.csv' + # Initialize variables to hold processed data + class_frequency_dict = {} + flag = None + time = None - # Reading the CSV file into a DataFrame - yes_no = pd.read_csv(yes_no_path) + # Process the latest ransomware type CSV + try: + if not os.path.exists(csv_file_path): + raise FileNotFoundError(f"CSV file not found at path: {csv_file_path}") - # # Extracting the value of 'Predicted Label' - flag =yes_no[yes_no.columns[-1]].iloc[0] - time = yes_no[yes_no.columns[-2]].iloc[0] - + # Load ransomware type CSV + df = pd.read_csv(csv_file_path) + print(f"Loaded ransomware type CSV: {csv_file_path}") + # Load mapping file + if not os.path.exists(mapping_file_path): + raise FileNotFoundError(f"Mapping file not found at path: {mapping_file_path}") - - return render(request, 'ransomware/ransomware.html' , context={ 'type' : class_frequency_dict, 'cpu' : json.dumps(cpu_data) , 'memory' : json.dumps(memory_data) , 'flag' : flag,'time' : time}) + mapping_df = pd.read_csv(mapping_file_path, header=None, names=['predicted_class', 'class_name']) + class_mapping = dict(zip(mapping_df['predicted_class'], mapping_df['class_name'])) + print("Loaded mapping file and created class mapping dictionary.") + + # Map predicted classes to class names + df['class_name'] = df['predicted_class'].map(class_mapping) + class_frequency = df['class_name'].value_counts() + + # Ensure all classes from mapping are present in the frequency dictionary + all_classes_df = pd.DataFrame({'class_name': mapping_df['class_name']}) + all_classes_df['frequency'] = all_classes_df['class_name'].map(class_frequency).fillna(0).astype(int) + class_frequency_dict = dict(zip(all_classes_df['class_name'], all_classes_df['frequency'])) + + print(f"Class frequency dictionary: {class_frequency_dict}") + + except FileNotFoundError as e: + print(f"FileNotFoundError: {str(e)}") + return JsonResponse({'error': str(e)}, status=404) + except Exception as e: + print(f"Exception while processing ransomware type CSV: {str(e)}") + return JsonResponse({'error': f"Error processing ransomware type CSV: {str(e)}"}, status=500) + + # Process the ransomware flag CSV + try: + if not os.path.exists(yes_no_path): + raise FileNotFoundError(f"Ransomware CSV file not found at path: {yes_no_path}") + + # Load ransomware flag CSV + yes_no = pd.read_csv(yes_no_path) + print('Loaded ransomware flag CSV:', yes_no) + + if yes_no.empty: + raise ValueError("Ransomware CSV file is empty.") + + # Extracting the value of 'Predicted Label' and 'Time' + flag = yes_no.iloc[0, -1] # Assuming 'Predicted Label' is the last column + time = yes_no.iloc[0, -2] # Assuming 'Time' is the second last column + + print(f"Extracted flag: {flag}, time: {time}") + + except FileNotFoundError as e: + print(f"FileNotFoundError: {str(e)}") + return JsonResponse({'error': str(e)}, status=404) + except Exception as e: + print(f"Exception while processing ransomware flag CSV: {str(e)}") + return JsonResponse({'error': f"Error processing ransomware flag CSV: {str(e)}"}, status=500) + + # Prepare context for rendering the template + context = { + 'type': class_frequency_dict, + 'cpu': json.dumps(cpu_data), + 'memory': json.dumps(memory_data), + 'flag': flag, + 'time': time + } + + return render(request, 'ransomware/ransomware.html', context=context) #================================================================================================== @@ -1589,9 +2027,44 @@ def ransomware_predictions(request): return JsonResponse({'message': 'File uploaded and overwritten successfully', 'file_path': save_path}) +# @api_view(['POST']) +# def ransomware_type_predictions(request): +# try: + +# csv_file = request.FILES.get('file') + +# if not csv_file: +# return JsonResponse({'error': 'No file provided'}, status=400) + +# if not csv_file.name.endswith('.csv'): +# return JsonResponse({'error': 'File is not CSV'}, status=400) + +# # Define the directory and file path where the CSV will be stored +# folder_path = os.path.join(settings.MEDIA_ROOT, 'ransomware_predictions') + +# # Make sure the directory exists +# if not os.path.exists(folder_path): +# os.makedirs(folder_path) + +# # Define the path for the file (always named 'latest_ransomware.csv') +# save_path = os.path.join(folder_path, 'latest_ransomware_type.csv') + +# # If the file already exists, remove it to ensure overwriting +# if os.path.exists(save_path): +# os.remove(save_path) + +# # Save the new file +# with open(save_path, 'wb+') as destination: +# for chunk in csv_file.chunks(): +# destination.write(chunk) + +# return JsonResponse({'message': 'File uploaded and overwritten successfully', 'file_path': save_path}) +# except Exception as e: +# print(e) + @api_view(['POST']) def ransomware_type_predictions(request): - try: + csv_file = request.FILES.get('file') @@ -1602,58 +2075,195 @@ def ransomware_type_predictions(request): return JsonResponse({'error': 'File is not CSV'}, status=400) # Define the directory and file path where the CSV will be stored - folder_path = os.path.join(settings.MEDIA_ROOT, 'ransomware_predictions') + # folder_path = os.path.join(settings.MEDIA_ROOT, 'ransomware_predictions') + + # # Make sure the directory exists + # if not os.path.exists(folder_path): + # os.makedirs(folder_path) + + # # Define the path for the file (always named 'latest_ransomware.csv') + # save_path = os.path.join(folder_path, 'latest_ransomware_type.csv') + + # # If the file already exists, remove it to ensure overwriting + # if os.path.exists(save_path): + # os.remove(save_path) + + # # Save the new file + # with open(save_path, 'wb+') as destination: + # for chunk in csv_file.chunks(): + # destination.write(chunk) + + # Extract user_id from the request (device_id is not needed now) + user_id = request.data.get('user_id') + + if not user_id: + return JsonResponse({'error': 'User ID is required'}, status=400) + + try: + # Retrieve the UserProfile based on the provided user_id + user_profile = UserProfile.objects.get(user__id=user_id) + print(user_profile) + + # Get the device IDs associated with the user + device_ids = get_device_ids_by_user_id(user_id) + print(f"Device IDs: {device_ids}") + + # Check if the user has devices associated with them + if not device_ids: + return JsonResponse({'error': 'No devices associated with the given user ID'}, status=400) + + # Assuming we want to use the first device associated with the user + device = Devices.objects.get(id=device_ids[-1]) + print(f"Device ID: {device.id}") + folder_path = os.path.join(settings.MEDIA_ROOT, 'ransomware_predictions', str(device.id)) + # folder_path = os.path.join(settings.MEDIA_ROOT, 'ransomware_predictions') # Make sure the directory exists - if not os.path.exists(folder_path): - os.makedirs(folder_path) + if not os.path.exists(folder_path): + os.makedirs(folder_path) - # Define the path for the file (always named 'latest_ransomware.csv') - save_path = os.path.join(folder_path, 'latest_ransomware_type.csv') + # Define the path for the file (always named 'latest_ransomware.csv') + save_path = os.path.join(folder_path, 'latest_ransomware_type.csv') - # If the file already exists, remove it to ensure overwriting - if os.path.exists(save_path): - os.remove(save_path) + # If the file already exists, remove it to ensure overwriting + if os.path.exists(save_path): + os.remove(save_path) - # Save the new file + # Save the new file + with open(save_path, 'wb+') as destination: + for chunk in csv_file.chunks(): + destination.write(chunk) + + + # Create the DdosPrediction record + rensomware_type_prediction = Rensomware_TypePrediction.objects.create( + device=device, + user=user_profile, + file_path=save_path + ) + + return JsonResponse({ + 'message': 'File uploaded and prediction saved successfully', + 'file_path': save_path, + 'prediction_id': rensomware_type_prediction.id + }) + + except UserProfile.DoesNotExist: + return JsonResponse({'error': 'User not found'}, status=404) + except Devices.DoesNotExist: + return JsonResponse({'error': 'Device not found'}, status=404) + + +def get_device_ids_by_user_id(user_id): + try: + # Get the UserProfile instance using the user ID + user_profile = UserProfile.objects.get(user__id=user_id) + print('user_profile', user_profile) + + # Retrieve all Devices associated with this UserProfile + devices = Devices.objects.filter(used_by=user_profile) + print('devices', devices) + + # Get the device IDs + device_ids = [device.id for device in devices] + return device_ids + except UserProfile.DoesNotExist: + return [] + +# @api_view(['POST']) +# def ddos_predictions(request): +# csv_file = request.FILES.get('file') + +# if not csv_file: +# return JsonResponse({'error': 'No file provided'}, status=400) + +# if not csv_file.name.endswith('.csv'): +# return JsonResponse({'error': 'File is not CSV'}, status=400) + +# # Define the directory and file path where the CSV will be stored +# folder_path = os.path.join(settings.MEDIA_ROOT, 'ddos_predictions') + +# # Make sure the directory exists +# if not os.path.exists(folder_path): +# os.makedirs(folder_path) + +# # Define the path for the file (always named 'latest_ransomware.csv') +# save_path = os.path.join(folder_path, 'predictions.csv') + +# # If the file already exists, remove it to ensure overwriting +# if os.path.exists(save_path): +# os.remove(save_path) + +# # Save the new file +# with open(save_path, 'wb+') as destination: +# for chunk in csv_file.chunks(): +# destination.write(chunk) + +# return JsonResponse({'message': 'File uploaded and overwritten successfully', 'file_path': save_path}) +from rest_framework.permissions import AllowAny +from rest_framework.decorators import permission_classes + +@api_view(['POST']) +@permission_classes([AllowAny]) + +def ddos_predictions(request): + # Check if a file is provided in the request + csv_file = request.FILES.get('file') + if not csv_file: + return JsonResponse({'error': 'No file provided'}, status=400) + + # Ensure the file is a CSV + if not csv_file.name.endswith('.csv'): + return JsonResponse({'error': 'File is not CSV'}, status=400) + + # Extract user_id from the request data + user_id = request.data.get('user_id') + device_ids = get_device_ids_by_user_id(user_id) + + # Check if the user has associated devices + if not device_ids: + return JsonResponse({'error': 'No devices associated with the given user ID'}, status=400) + + try: + # Retrieve the UserProfile for the logged-in user + user_profile = UserProfile.objects.get(user__id=user_id) + + # Get the most recent device associated with the user + device = Devices.objects.get(id=device_ids[-1]) + + # Define the directory path: MEDIA_ROOT/ddos_predictions/ + folder_path = os.path.join(settings.MEDIA_ROOT, 'ddos_predictions', str(device.id)) + # folder_path = os.path.join(settings.MEDIA_ROOT, 'ddos_predictions') + os.makedirs(folder_path, exist_ok=True) # Ensure the folder exists + + # Define the file path: predictions.csv + file_name = 'predictions.csv' + save_path = os.path.join(folder_path, file_name) + + # Save the file in chunks with open(save_path, 'wb+') as destination: for chunk in csv_file.chunks(): destination.write(chunk) - return JsonResponse({'message': 'File uploaded and overwritten successfully', 'file_path': save_path}) + # Create a DdosPrediction record with the relative file path + relative_path = os.path.relpath(save_path, settings.MEDIA_ROOT) # Save the relative path + ddos_prediction = DdosPrediction.objects.create( + device=device, + user=user_profile, + file_path=relative_path + ) + + # Return a success response + return JsonResponse({ + 'message': 'File uploaded and prediction saved successfully', + 'file_path': relative_path, + 'prediction_id': ddos_prediction.id + }) + + except UserProfile.DoesNotExist: + return JsonResponse({'error': 'User profile not found'}, status=404) except Exception as e: - print(e) - -@api_view(['POST']) -def ddos_predictions(request): - csv_file = request.FILES.get('file') - - if not csv_file: - return JsonResponse({'error': 'No file provided'}, status=400) - - if not csv_file.name.endswith('.csv'): - return JsonResponse({'error': 'File is not CSV'}, status=400) - - # Define the directory and file path where the CSV will be stored - folder_path = os.path.join(settings.MEDIA_ROOT, 'ddos_predictions') - - # Make sure the directory exists - if not os.path.exists(folder_path): - os.makedirs(folder_path) - - # Define the path for the file (always named 'latest_ransomware.csv') - save_path = os.path.join(folder_path, 'predictions.csv') - - # If the file already exists, remove it to ensure overwriting - if os.path.exists(save_path): - os.remove(save_path) - - # Save the new file - with open(save_path, 'wb+') as destination: - for chunk in csv_file.chunks(): - destination.write(chunk) - - return JsonResponse({'message': 'File uploaded and overwritten successfully', 'file_path': save_path}) + return JsonResponse({'error': f'An unexpected error occurred: {str(e)}'}, status=500) @api_view(['POST']) def usage_log(request): diff --git a/Device/__pycache__/urls.cpython-310.pyc b/Device/__pycache__/urls.cpython-310.pyc index 4968136..7b7ae0b 100644 Binary files a/Device/__pycache__/urls.cpython-310.pyc and b/Device/__pycache__/urls.cpython-310.pyc differ diff --git a/Device/__pycache__/views.cpython-310.pyc b/Device/__pycache__/views.cpython-310.pyc index 893a154..baebfcc 100644 Binary files a/Device/__pycache__/views.cpython-310.pyc and b/Device/__pycache__/views.cpython-310.pyc differ diff --git a/helpdesk/management/__pycache__/__init__.cpython-310.pyc b/helpdesk/management/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000..c8f64de Binary files /dev/null and b/helpdesk/management/__pycache__/__init__.cpython-310.pyc differ diff --git a/malware/__pycache__/models.cpython-310.pyc b/malware/__pycache__/models.cpython-310.pyc index 06e55f9..9db751d 100644 Binary files a/malware/__pycache__/models.cpython-310.pyc and b/malware/__pycache__/models.cpython-310.pyc differ diff --git a/malware/__pycache__/views.cpython-310.pyc b/malware/__pycache__/views.cpython-310.pyc index 668074a..5a3e02d 100644 Binary files a/malware/__pycache__/views.cpython-310.pyc and b/malware/__pycache__/views.cpython-310.pyc differ diff --git a/malware/migrations/0003_malwarepredictionsdevice.py b/malware/migrations/0003_malwarepredictionsdevice.py new file mode 100644 index 0000000..d7a01eb --- /dev/null +++ b/malware/migrations/0003_malwarepredictionsdevice.py @@ -0,0 +1,26 @@ +# Generated by Django 5.1.3 on 2024-12-10 09:51 + +import django.db.models.deletion +from django.db import migrations, models + + +class Migration(migrations.Migration): + + dependencies = [ + ('Accounts', '0003_userprofile_company_name'), + ('Device', '0007_devices_mac_address_devices_unique_id'), + ('malware', '0002_malwareprediction_model_type'), + ] + + operations = [ + migrations.CreateModel( + name='MalwarePredictionsDevice', + fields=[ + ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), + ('file_path', models.FileField(upload_to='malware_predictions/')), + ('uploaded_at', models.DateTimeField(auto_now_add=True)), + ('device', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='Device.devices')), + ('user', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='Accounts.userprofile')), + ], + ), + ] diff --git a/malware/migrations/__pycache__/0003_malwarepredictionsdevice.cpython-310.pyc b/malware/migrations/__pycache__/0003_malwarepredictionsdevice.cpython-310.pyc new file mode 100644 index 0000000..8fdde9a Binary files /dev/null and b/malware/migrations/__pycache__/0003_malwarepredictionsdevice.cpython-310.pyc differ diff --git a/malware/models.py b/malware/models.py index f97af8c..e0e4706 100644 --- a/malware/models.py +++ b/malware/models.py @@ -1,4 +1,6 @@ from django.db import models +from Device .models import Devices +from Accounts.models import UserProfile # Create your models here. @@ -19,3 +21,13 @@ class MalwarePrediction(models.Model): def __str__(self): return f"{self.process_name} - {self.predicted_malware} - {self.get_model_type_display()}" + +class MalwarePredictionsDevice(models.Model): + device = models.ForeignKey(Devices, on_delete=models.CASCADE,null=True) + user = models.ForeignKey(UserProfile, on_delete=models.CASCADE , null=True) # Add this field to reference the user + file_path = models.FileField(upload_to='malware_predictions/') + uploaded_at = models.DateTimeField(auto_now_add=True) + + def __str__(self): + return f"Prediction for {self.device.device_name} by {self.user.user.username} at {self.uploaded_at}" + \ No newline at end of file diff --git a/malware/views.py b/malware/views.py index 40da403..7c89c32 100644 --- a/malware/views.py +++ b/malware/views.py @@ -18,12 +18,386 @@ from django.core.files.storage import default_storage from rest_framework.parsers import MultiPartParser from django.conf import settings from django.http import HttpResponse -from .models import MalwarePrediction +from .models import MalwarePrediction,MalwarePredictionsDevice from .serializers import MalwarePredictionSerializer +from Device.models import Devices +from Accounts.models import UserProfile +from django.utils import timezone +from django.http import JsonResponse +from django.utils.decorators import method_decorator +from django.views.decorators.csrf import csrf_exempt class MalwarePredictionAPIView(APIView): parser_classes = [MultiPartParser] # To handle file uploads + @staticmethod + def get_device_ids_by_user_id(user_id): + try: + # Get the UserProfile instance using the user ID + user_profile = UserProfile.objects.get(user__id=user_id) + print('user_profile', user_profile) + + # Retrieve all Devices associated with this UserProfile + devices = Devices.objects.filter(used_by=user_profile) + print('devices', devices) + + # Get the device IDs + device_ids = [device.id for device in devices] + return device_ids + except UserProfile.DoesNotExist: + return [] + + def post(self, request, *args, **kwargs): + if 'csv_file' not in request.FILES: + return Response({"error": "No file provided"}, status=status.HTTP_400_BAD_REQUEST) + + csv_file = request.FILES['csv_file'] + user_id= request.data.get('user_id') + if not user_id : + return Response({"error": "User ID is required"}, status=status.HTTP_400_BAD_REQUEST) + + device_ids = self.get_device_ids_by_user_id(user_id) + print(f"Device IDs: {device_ids}") + if not device_ids: + return Response({'error': 'No devices associated with the given user ID'}, status=status.HTTP_400_BAD_REQUEST) + + try: + # device = Devices.objects.filter(id__in=device_ids).order_by('-created_at').first() # Use the first device ID + + + # Get the most recent device associated with the user + device = Devices.objects.get(id=device_ids[-1]) + print(f"Device ID: {device.id}") + except Devices.DoesNotExist: + return Response({"error": "Device not found for the given device ID"}, status=status.HTTP_400_BAD_REQUEST) + + try: + # Define the temp directory path + temp_dir = os.path.join(settings.MEDIA_ROOT, 'malware_predictions',str(device.id)) + + # Create the 'temp' directory if it doesn't exist + if not os.path.exists(temp_dir): + os.makedirs(temp_dir) + + # Save the file temporarily + temp_file_path = os.path.join(temp_dir, csv_file.name) + with default_storage.open(temp_file_path, 'wb+') as destination: + for chunk in csv_file.chunks(): + destination.write(chunk) + + # Read the CSV file with headers + df = pd.read_csv(temp_file_path) + + # Extract column names from the CSV + actual_columns = df.columns.tolist() + + except Exception as e: + return Response({"error": "Could not read the CSV file", "details": str(e)}, status=status.HTTP_400_BAD_REQUEST) + + # Define the expected column names + expected_columns = ['process_name', 'class', 'probability_of_malware'] + + # Mapping logic + if actual_columns != expected_columns: + # Map actual column names to expected ones + column_mapping = dict(zip(actual_columns, expected_columns)) + df.rename(columns=column_mapping, inplace=True) + + # Continue with processing the dataframe... + + records_saved = 0 + for _, row in df.iterrows(): + process_name = row['process_name'] + process_class = row['class'] + probability = row['probability_of_malware'] + + MalwarePrediction.objects.create( + process_name=process_name, + process_class=process_class, + probability_of_malware=probability, + ) + records_saved += 1 + # print(data_sent,"dataaaaaa") + + return Response({"message": f"{records_saved} records saved successfully!"}, status=status.HTTP_201_CREATED) + + + + + + def get(self, request, *args, **kwargs): + # Query all MalwarePrediction records from the database + predictions = MalwarePrediction.objects.all() + + if not predictions.exists(): + return Response({"error": "No data available to generate graph."}, status=status.HTTP_404_NOT_FOUND) + + # Create a DataFrame from the queryset + data = { + 'process_name': [p.process_name for p in predictions], + 'class': [p.process_class for p in predictions], + 'probability_of_malware': [p.probability_of_malware for p in predictions] + } + df = pd.DataFrame(data) + + # Plot using seaborn or matplotlib + plt.figure(figsize=(10, 6)) + + # Create a barplot where the class is on the x-axis and the probability is on the y-axis + sns.barplot( + data=df, + x='class', # Independent variable (x-axis) + y='probability_of_malware', # Dependent variable (y-axis) + ci=None, # No confidence intervals + palette='Set2' # Use a color palette for different classes + ) + + plt.title('Malware Probability by Class') + plt.xlabel('Class') + plt.ylabel('Probability of Malware') + plt.tight_layout() + + # Save the plot to a bytes buffer + buf = io.BytesIO() + plt.savefig(buf, format='png') + buf.seek(0) + + # Return the image as a response + return HttpResponse(buf, content_type='image/png') + + + + +# class MalwarePredictionAPIView(APIView): +# parser_classes = [MultiPartParser] # To handle file uploads + +# @staticmethod +# def get_device_ids_by_user_id(user_id): +# try: +# # Get the UserProfile instance using the user ID +# user_profile = UserProfile.objects.get(user__id=user_id) +# print('user_profile', user_profile) + +# # Retrieve all Devices associated with this UserProfile +# devices = Devices.objects.filter(used_by=user_profile) +# print('devices', devices) + +# # Get the device IDs +# device_ids = [device.id for device in devices] +# return device_ids +# except UserProfile.DoesNotExist: +# return [] + +# def post(self, request, *args, **kwargs): +# if 'csv_file' not in request.FILES: +# return Response({"error": "No file provided"}, status=status.HTTP_400_BAD_REQUEST) + +# csv_file = request.FILES['csv_file'] + +# # Retrieve user ID from the request +# user_id = request.data.get('user_id') # Ensure 'user_id' is being sent in the request body +# print("user_id ", user_id) + +# if not user_id: +# return Response({"error": "User ID is required"}, status=status.HTTP_400_BAD_REQUEST) + +# # Get the device IDs associated with the user +# device_ids = self.get_device_ids_by_user_id(user_id) +# print(f"Device IDs: {device_ids}") + +# # Fetch the first associated device for the user +# if not device_ids: +# return Response({'error': 'No devices associated with the given user ID'}, status=status.HTTP_400_BAD_REQUEST) + +# try: +# # device = Devices.objects.filter(id__in=device_ids).order_by('-created_at').first() # Use the first device ID +# device = Devices.objects.filter(used_by__user=request.user).order_by('-id').first() +# print(f"Device ID: {device.id}") +# except Devices.DoesNotExist: +# return Response({"error": "Device not found for the given device ID"}, status=status.HTTP_400_BAD_REQUEST) + +# try: +# user_profile = UserProfile.objects.get(user__id=user_id) +# print(user_profile) +# except UserProfile.DoesNotExist: +# return Response({"error": "User profile not found"}, status=status.HTTP_400_BAD_REQUEST) + +# try: +# # Define the temp directory path using the device ID +# temp_dir = os.path.join(settings.MEDIA_ROOT, 'malware_predictions') + +# # Create the temp directory if it doesn't exist +# if not os.path.exists(temp_dir): +# os.makedirs(temp_dir) + +# # Save the file temporarily +# temp_file_path = os.path.join(temp_dir, csv_file.name) +# with default_storage.open(temp_file_path, 'wb+') as destination: +# for chunk in csv_file.chunks(): +# destination.write(chunk) + +# # Read the CSV file with headers +# df = pd.read_csv(temp_file_path) + +# # Extract column names from the CSV +# actual_columns = df.columns.tolist() +# except Exception as e: +# return Response({"error": "Could not read the CSV file", "details": str(e)}, status=status.HTTP_400_BAD_REQUEST) + +# # Define the expected column names +# expected_columns = ['process_name', 'class', 'probability_of_malware'] + +# # Mapping logic +# if actual_columns != expected_columns: +# # Map actual column names to expected ones +# column_mapping = dict(zip(actual_columns, expected_columns)) +# df.rename(columns=column_mapping, inplace=True) + +# # Save the data to the database +# records_saved = 0 +# for _, row in df.iterrows(): +# try: +# process_name = row['process_name'] +# process_class = row['class'] +# probability = float(row['probability_of_malware']) # Ensure it's a number +# except ValueError: +# return Response({ +# "error": f"Invalid value in 'probability_of_malware': {row['probability_of_malware']}" +# }, status=status.HTTP_400_BAD_REQUEST) + +# # MalwarePredictionsDevice.objects.create( +# # device=device, # Pass the Devices instance here +# # user=user_profile, # This will reference the user related to the device +# # file_path=temp_file_path, # The path to the uploaded file +# # ) +# MalwarePrediction.objects.create( +# process_name=process_name, +# process_class=process_class, +# probability_of_malware=probability, +# ) +# records_saved += 1 + +# return Response({ +# "message": f"{records_saved} records saved successfully!", +# }, status=status.HTTP_201_CREATED) + + +# class MalwarePredictionAPIView(APIView): +# parser_classes = [MultiPartParser] # To handle file uploads +# @staticmethod +# def get_device_ids_by_user_id(user_id): +# try: +# # Get the UserProfile instance using the user ID +# user_profile = UserProfile.objects.get(user__id=user_id) +# print('user_profile', user_profile) + +# # Retrieve all Devices associated with this UserProfile +# devices = Devices.objects.filter(used_by=user_profile) +# print('devices', devices) + +# # Get the device IDs +# device_ids = [device.id for device in devices] +# return device_ids +# except UserProfile.DoesNotExist: +# return [] + +# def post(self, request, *args, **kwargs): +# if 'csv_file' not in request.FILES: +# return Response({"error": "No file provided"}, status=status.HTTP_400_BAD_REQUEST) + +# csv_file = request.FILES.get('csv_file') +# if not csv_file: +# return Response({"error": "No CSV file provided"}, status=status.HTTP_400_BAD_REQUEST) + +# user_id = request.data.get('user_id') +# if not user_id: +# return Response({"error": "User ID is required"}, status=status.HTTP_400_BAD_REQUEST) + +# # Retrieve associated device IDs for the user +# device_ids = self.get_device_ids_by_user_id(user_id) +# print(device_ids) +# if not device_ids: +# return Response({"error": "No devices associated with the given user ID"}, status=status.HTTP_400_BAD_REQUEST) + +# # Try to get therecent device associated with the user +# try: +# device = Devices.objects.get(id=device_ids[-1]) +# except Devices.DoesNotExist: +# return Response({"error": "Device not found for the given device ID"}, status=status.HTTP_400_BAD_REQUEST) + +# # Define the temp directory path for saving the file +# temp_dir = os.path.join(settings.MEDIA_ROOT, 'malware_predictions', f'device_{device.id}') + +# # Create the 'temp' directory if it doesn't exist +# if not os.path.exists(temp_dir): +# os.makedirs(temp_dir) + +# # Save the file temporarily +# temp_file_path = os.path.join(temp_dir, csv_file.name) +# try: +# with default_storage.open(temp_file_path, 'wb+') as destination: +# for chunk in csv_file.chunks(): +# destination.write(chunk) +# except Exception as e: +# return Response({"error": "Failed to save the file", "details": str(e)}, status=status.HTTP_500_INTERNAL_SERVER_ERROR) + +# # Read the CSV file with headers +# try: +# df = pd.read_csv(temp_file_path) +# actual_columns = df.columns.tolist() +# except Exception as e: +# return Response({"error": "Could not read the CSV file", "details": str(e)}, status=status.HTTP_400_BAD_REQUEST) + +# # Define the expected column names +# expected_columns = ['process_name', 'class', 'probability_of_malware'] + +# # Validate and map columns +# if actual_columns != expected_columns: +# if len(actual_columns) == len(expected_columns): +# column_mapping = dict(zip(actual_columns, expected_columns)) +# df.rename(columns=column_mapping, inplace=True) +# else: +# return Response({"error": "CSV columns do not match expected format"}, status=status.HTTP_400_BAD_REQUEST) + +# # Ensure the user profile exists +# try: +# user_profile = UserProfile.objects.get(user__id=user_id) +# except UserProfile.DoesNotExist: +# return Response({"error": "User profile not found"}, status=status.HTTP_400_BAD_REQUEST) + +# # Save the predictions and create the related record +# records_saved = 0 +# for _, row in df.iterrows(): +# process_name = row['process_name'] +# process_class = row['class'] +# probability = row['probability_of_malware'] + +# try: +# # Save malware prediction +# MalwarePrediction.objects.create( +# process_name=process_name, +# process_class=process_class, +# probability_of_malware=probability, +# ) + +# # Save the device association +# MalwarePredictionsDevice.objects.create( +# device=device, +# user=user_profile, +# file_path=temp_file_path, +# ) +# records_saved += 1 +# except Exception as e: +# return Response({"error": "Failed to save record", "details": str(e)}, status=status.HTTP_500_INTERNAL_SERVER_ERROR) + +# return Response({ +# "message": f"{records_saved} records saved successfully!" +# }, status=status.HTTP_201_CREATED) + + +class KNeighborsModelView(APIView): + parser_classes = [MultiPartParser] # To handle file uploads + def post(self, request, *args, **kwargs): if 'csv_file' not in request.FILES: @@ -75,18 +449,14 @@ class MalwarePredictionAPIView(APIView): process_name=process_name, process_class=process_class, probability_of_malware=probability, + model_type=1 ) records_saved += 1 - return Response({"message": f"{records_saved} records saved successfully!"}, status=status.HTTP_201_CREATED) - - - - - + return Response({"message": " knn file saved successfully!"}, status=status.HTTP_201_CREATED) def get(self, request, *args, **kwargs): # Query all MalwarePrediction records from the database - predictions = MalwarePrediction.objects.all() + predictions = MalwarePrediction.objects.filter(model_type=1) if not predictions.exists(): return Response({"error": "No data available to generate graph."}, status=status.HTTP_404_NOT_FOUND) @@ -126,6 +496,313 @@ class MalwarePredictionAPIView(APIView): + + +class RandomForestModelView(APIView): + parser_classes = [MultiPartParser] # To handle file uploads + + + def post(self, request, *args, **kwargs): + if 'csv_file' not in request.FILES: + return Response({"error": "No file provided"}, status=status.HTTP_400_BAD_REQUEST) + + csv_file = request.FILES['csv_file'] + + try: + # Define the temp directory path + temp_dir = os.path.join(settings.MEDIA_ROOT, 'temp') + + # Create the 'temp' directory if it doesn't exist + if not os.path.exists(temp_dir): + os.makedirs(temp_dir) + + # Save the file temporarily + temp_file_path = os.path.join(temp_dir, csv_file.name) + with default_storage.open(temp_file_path, 'wb+') as destination: + for chunk in csv_file.chunks(): + destination.write(chunk) + + # Read the CSV file with headers + df = pd.read_csv(temp_file_path) + + # Extract column names from the CSV + actual_columns = df.columns.tolist() + + except Exception as e: + return Response({"error": "Could not read the CSV file", "details": str(e)}, status=status.HTTP_400_BAD_REQUEST) + + # Define the expected column names + expected_columns = ['process_name', 'class', 'probability_of_malware'] + + # Mapping logic + if actual_columns != expected_columns: + # Map actual column names to expected ones + column_mapping = dict(zip(actual_columns, expected_columns)) + df.rename(columns=column_mapping, inplace=True) + + # Continue with processing the dataframe... + records_saved = 0 + for _, row in df.iterrows(): + process_name = row['process_name'] + process_class = row['class'] + probability = row['probability_of_malware'] + + # Save the row to the database + MalwarePrediction.objects.create( + process_name=process_name, + process_class=process_class, + probability_of_malware=probability, + model_type=2 + ) + records_saved += 1 + + return Response({"message": " RandomForest file saved successfully!"}, status=status.HTTP_201_CREATED) + def get(self, request, *args, **kwargs): + # Query all MalwarePrediction records from the database + predictions = MalwarePrediction.objects.filter(model_type=2) + + if not predictions.exists(): + return Response({"error": "No data available to generate graph."}, status=status.HTTP_404_NOT_FOUND) + + # Create a DataFrame from the queryset + data = { + 'process_name': [p.process_name for p in predictions], + 'class': [p.process_class for p in predictions], + 'probability_of_malware': [p.probability_of_malware for p in predictions] + } + df = pd.DataFrame(data) + + # Plot using seaborn or matplotlib + plt.figure(figsize=(10, 6)) + + # Create a barplot where the class is on the x-axis and the probability is on the y-axis + sns.barplot( + data=df, + x='class', # Independent variable (x-axis) + y='probability_of_malware', # Dependent variable (y-axis) + ci=None, # No confidence intervals + palette='Set2' # Use a color palette for different classes + ) + + plt.title('Malware Probability by Class') + plt.xlabel('Class') + plt.ylabel('Probability of Malware') + plt.tight_layout() + + # Save the plot to a bytes buffer + buf = io.BytesIO() + plt.savefig(buf, format='png') + buf.seek(0) + + # Return the image as a response + return HttpResponse(buf, content_type='image/png') + + + +class XGBModelView(APIView): + + parser_classes = [MultiPartParser] # To handle file uploads + + + def post(self, request, *args, **kwargs): + if 'csv_file' not in request.FILES: + return Response({"error": "No file provided"}, status=status.HTTP_400_BAD_REQUEST) + + csv_file = request.FILES['csv_file'] + + try: + # Define the temp directory path + temp_dir = os.path.join(settings.MEDIA_ROOT, 'temp') + + # Create the 'temp' directory if it doesn't exist + if not os.path.exists(temp_dir): + os.makedirs(temp_dir) + + # Save the file temporarily + temp_file_path = os.path.join(temp_dir, csv_file.name) + with default_storage.open(temp_file_path, 'wb+') as destination: + for chunk in csv_file.chunks(): + destination.write(chunk) + + # Read the CSV file with headers + df = pd.read_csv(temp_file_path) + + # Extract column names from the CSV + actual_columns = df.columns.tolist() + + except Exception as e: + return Response({"error": "Could not read the CSV file", "details": str(e)}, status=status.HTTP_400_BAD_REQUEST) + + # Define the expected column names + expected_columns = ['process_name', 'class', 'probability_of_malware'] + + # Mapping logic + if actual_columns != expected_columns: + # Map actual column names to expected ones + column_mapping = dict(zip(actual_columns, expected_columns)) + df.rename(columns=column_mapping, inplace=True) + + # Continue with processing the dataframe... + records_saved = 0 + for _, row in df.iterrows(): + process_name = row['process_name'] + process_class = row['class'] + probability = row['probability_of_malware'] + + # Save the row to the database + MalwarePrediction.objects.create( + process_name=process_name, + process_class=process_class, + probability_of_malware=probability, + model_type=3 + ) + records_saved += 1 + + return Response({"message": " XGB file saved successfully!"}, status=status.HTTP_201_CREATED) + def get(self, request, *args, **kwargs): + # Query all MalwarePrediction records from the database + predictions = MalwarePrediction.objects.filter(model_type=3) + + if not predictions.exists(): + return Response({"error": "No data available to generate graph."}, status=status.HTTP_404_NOT_FOUND) + + # Create a DataFrame from the queryset + data = { + 'process_name': [p.process_name for p in predictions], + 'class': [p.process_class for p in predictions], + 'probability_of_malware': [p.probability_of_malware for p in predictions] + } + df = pd.DataFrame(data) + + # Plot using seaborn or matplotlib + plt.figure(figsize=(10, 6)) + + # Create a barplot where the class is on the x-axis and the probability is on the y-axis + sns.barplot( + data=df, + x='class', # Independent variable (x-axis) + y='probability_of_malware', # Dependent variable (y-axis) + ci=None, # No confidence intervals + palette='Set2' # Use a color palette for different classes + ) + + plt.title('Malware Probability by Class') + plt.xlabel('Class') + plt.ylabel('Probability of Malware') + plt.tight_layout() + + # Save the plot to a bytes buffer + buf = io.BytesIO() + plt.savefig(buf, format='png') + buf.seek(0) + + # Return the image as a response + return HttpResponse(buf, content_type='image/png') + + +class SGDModelView(APIView): + + + parser_classes = [MultiPartParser] # To handle file uploads + + + def post(self, request, *args, **kwargs): + if 'csv_file' not in request.FILES: + return Response({"error": "No file provided"}, status=status.HTTP_400_BAD_REQUEST) + + csv_file = request.FILES['csv_file'] + + try: + # Define the temp directory path + temp_dir = os.path.join(settings.MEDIA_ROOT, 'temp') + + # Create the 'temp' directory if it doesn't exist + if not os.path.exists(temp_dir): + os.makedirs(temp_dir) + + # Save the file temporarily + temp_file_path = os.path.join(temp_dir, csv_file.name) + with default_storage.open(temp_file_path, 'wb+') as destination: + for chunk in csv_file.chunks(): + destination.write(chunk) + + # Read the CSV file with headers + df = pd.read_csv(temp_file_path) + + # Extract column names from the CSV + actual_columns = df.columns.tolist() + + except Exception as e: + return Response({"error": "Could not read the CSV file", "details": str(e)}, status=status.HTTP_400_BAD_REQUEST) + + # Define the expected column names + expected_columns = ['process_name', 'class', 'probability_of_malware'] + + # Mapping logic + if actual_columns != expected_columns: + # Map actual column names to expected ones + column_mapping = dict(zip(actual_columns, expected_columns)) + df.rename(columns=column_mapping, inplace=True) + + # Continue with processing the dataframe... + records_saved = 0 + for _, row in df.iterrows(): + process_name = row['process_name'] + process_class = row['class'] + probability = row['probability_of_malware'] + + # Save the row to the database + MalwarePrediction.objects.create( + process_name=process_name, + process_class=process_class, + probability_of_malware=probability, + model_type=4 + ) + records_saved += 1 + + return Response({"message": " SGD file saved successfully!"}, status=status.HTTP_201_CREATED) + def get(self, request, *args, **kwargs): + # Query all MalwarePrediction records from the database + predictions = MalwarePrediction.objects.filter(model_type=4) + + if not predictions.exists(): + return Response({"error": "No data available to generate graph."}, status=status.HTTP_404_NOT_FOUND) + + # Create a DataFrame from the queryset + data = { + 'process_name': [p.process_name for p in predictions], + 'class': [p.process_class for p in predictions], + 'probability_of_malware': [p.probability_of_malware for p in predictions] + } + df = pd.DataFrame(data) + + # Plot using seaborn or matplotlib + plt.figure(figsize=(10, 6)) + + # Create a barplot where the class is on the x-axis and the probability is on the y-axis + sns.barplot( + data=df, + x='class', # Independent variable (x-axis) + y='probability_of_malware', # Dependent variable (y-axis) + ci=None, # No confidence intervals + palette='Set2' # Use a color palette for different classes + ) + + plt.title('Malware Probability by Class') + plt.xlabel('Class') + plt.ylabel('Probability of Malware') + plt.tight_layout() + + # Save the plot to a bytes buffer + buf = io.BytesIO() + plt.savefig(buf, format='png') + buf.seek(0) + + # Return the image as a response + return HttpResponse(buf, content_type='image/png') + + + class KNeighborsModelView(APIView): parser_classes = [MultiPartParser] # To handle file uploads diff --git a/templates/malware/malware.html b/templates/malware/malware.html index 4d5c510..bdc17a1 100644 --- a/templates/malware/malware.html +++ b/templates/malware/malware.html @@ -13,6 +13,11 @@
+ {% if message %} + + {% endif %}

Alerts and Alarms

@@ -285,14 +290,25 @@ 
-
                         {% for line in logs %}
+                        
+                        
                         
{{line}}

+                        
                         {% endfor %}
-
+
+ +
@@ -303,7 +319,7 @@ - +