this is multiple device support log

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')),
+            ],
+        ),
+    ]

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})"
+        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}"
+    
+    

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'),

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')),
+            ],
+        ),
+    ]

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}"
+    

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

templates/malware/malware.html

@@ -13,6 +13,11 @@
     </head>    
     <body>    
         <div class="tabSection">
+            {% if message %}
+                <div class="alert alert-warning" role="alert">
+                    {{ message }}
+                </div>
+            {% endif %}
             <div class="tabMalwareTitle">
                 <p class="tabSectionMalware tabSectionActiveMalware">Alerts and Alarms</p>
                 <!-- <p class="tabSectionMalware ">Alerts and Alarms</p> -->
@@ -285,14 +290,25 @@
                 </div>
                 <div class="tabSectionDetails">
                     <pre id="MalwareLogActivity">
-
                         {% for line in logs %}
+                        <script>
+                            setInterval(fetchLogs, 2000);
+                            
+                        </script>
+                        
                         <p>{{line}}</p>
+                        
                         {% endfor %}
-
+                        
                     </pre>
 
                 </div>
+                <!-- {% if message %}
+                    <div class="alert alert-warning" role="alert">
+                        {{ message }}
+                    </div>
+                {% endif %} -->
+
             </div>
         </div>
    
@@ -303,7 +319,7 @@
         <script src="{% static 'malware/js/PredictedChart.js' %}"></script>
         <script type="application/json" id="class-frequency-data">{{ class_frequency|safe }}</script>
         
-        <script type="text/javascript">
+        <!-- <script type="text/javascript">
             google.charts.load("current", { packages: ['corechart'] });
             google.charts.setOnLoadCallback(drawChart);
     
@@ -356,7 +372,145 @@
                 var chart = new google.visualization.ColumnChart(document.getElementById("barMalwareChart"));
                 chart.draw(view, options);
             }
+        </script> -->
+        
+        <!-- <script type="text/javascript">
+            google.charts.load("current", { packages: ['corechart'] });
+            google.charts.setOnLoadCallback(drawChart);
+        
+            function drawChart() {
+                // Function to fetch and draw the chart
+                function updateChart() {
+                    // Use the Django context variable to populate the frequencies
+                    var classData = document.getElementById('class-frequency-data').textContent
+                    classData = classData.replace(/'/g, '"');
+                    var classFrequency = JSON.parse(classData);
+                    
+                    var data = google.visualization.arrayToDataTable([
+                        ["Element", "Density", { role: "style" }],
+                        ["Ramnit", parseInt(classFrequency['Ramnit']), "#000080"],
+                        ["Lollipop", parseInt(classFrequency['LolliPop']), "#0000ff"],
+                        ["Kelihos_ver3", parseInt(classFrequency['Kelihos_ver3']), "#0081ff"],
+                        ["Vundo", parseInt(classFrequency['Vundo']), "#17ffe2"],
+                        ["Simda", parseInt(classFrequency['Simda']), "#7bff7b"],
+                        ["Tracur", parseInt(classFrequency['Tracur']), "#e3ff15"],
+                        ["Kelihos_ver1", parseInt(classFrequency['Kelihos_ver1']), "#ff9801"],
+                        ["Obfuscator.ACY", parseInt(classFrequency['Obfuscator.ACY']), "#ff2200"],
+                        ["Gatak", parseInt(classFrequency['Gatak']), "#810000"],
+                    ]);
+        
+                    var view = new google.visualization.DataView(data);
+                    view.setColumns([0, 1, 2]);
+        
+                    var options = {
+                        title: "",
+                        height: 430,
+                        width: 900,
+                        legend: { position: "none" },
+                        backgroundColor: '#0c212b',
+                        tooltip: { trigger: 'none' },
+                        vAxis: {
+                            title: 'Count',
+                            viewWindow: {
+                                min: 0,
+                                max: 200
+                            },
+                            ticks: [0, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200],
+                        },
+                        hAxis: { 
+                            title: 'Class',
+                            slantedText: true, // Tilt the text to prevent collisions
+                        },
+                        legend: { position: 'none' },
+                    };
+        
+                    var chart = new google.visualization.ColumnChart(document.getElementById("barMalwareChart"));
+                    chart.draw(view, options);
+                }
+        
+                // Initial draw
+                updateChart();
+        
+                // Set up real-time updates every 5 seconds
+                setInterval(updateChart, 1000);
+            }
+        </script> -->
+        
+        
+        <script type="text/javascript">
+            google.charts.load("current", { packages: ['corechart'] });
+            google.charts.setOnLoadCallback(drawChart);
+        
+            function drawChart() {
+                // Function to fetch and draw the chart
+                function updateChart() {
+                    // Use the Django context variable to populate the frequencies
+                    var classData = document.getElementById('class-frequency-data').textContent;
+                    classData = classData.replace(/'/g, '"');
+                    var classFrequency = JSON.parse(classData);
+                    
+                    // Check if classFrequency is empty or contains invalid data
+                    var isEmpty = Object.values(classFrequency).every(function(value) {
+                        return value === 0 || value === null || value === undefined;
+                    });
+        
+                    // If data is empty, show a placeholder or empty graph
+                    if (isEmpty) {
+                        document.getElementById("barMalwareChart").innerHTML = "<h3>No data available to display</h3>";
+                        return;  // Stop further execution if no data
+                    }
+        
+                    // Prepare the data for the chart
+                    var data = google.visualization.arrayToDataTable([
+                        ["Element", "Density", { role: "style" }],
+                        ["Ramnit", parseInt(classFrequency['Ramnit']), "#000080"],
+                        ["Lollipop", parseInt(classFrequency['LolliPop']), "#0000ff"],
+                        ["Kelihos_ver3", parseInt(classFrequency['Kelihos_ver3']), "#0081ff"],
+                        ["Vundo", parseInt(classFrequency['Vundo']), "#17ffe2"],
+                        ["Simda", parseInt(classFrequency['Simda']), "#7bff7b"],
+                        ["Tracur", parseInt(classFrequency['Tracur']), "#e3ff15"],
+                        ["Kelihos_ver1", parseInt(classFrequency['Kelihos_ver1']), "#ff9801"],
+                        ["Obfuscator.ACY", parseInt(classFrequency['Obfuscator.ACY']), "#ff2200"],
+                        ["Gatak", parseInt(classFrequency['Gatak']), "#810000"],
+                    ]);
+        
+                    var view = new google.visualization.DataView(data);
+                    view.setColumns([0, 1, 2]);
+        
+                    var options = {
+                        title: "",
+                        height: 430,
+                        width: 900,
+                        legend: { position: "none" },
+                        backgroundColor: '#0c212b',
+                        tooltip: { trigger: 'none' },
+                        vAxis: {
+                            title: 'Count',
+                            viewWindow: {
+                                min: 0,
+                                max: 200
+                            },
+                            ticks: [0, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200],
+                        },
+                        hAxis: { 
+                            title: 'Class',
+                            slantedText: true, // Tilt the text to prevent collisions
+                        },
+                        legend: { position: 'none' },
+                    };
+        
+                    var chart = new google.visualization.ColumnChart(document.getElementById("barMalwareChart"));
+                    chart.draw(view, options);
+                }
+        
+                // Initial draw
+                updateChart();
+        
+                // Set up real-time updates every 5 seconds
+                setInterval(updateChart, 1000);
+            }
         </script>
+        
         <script type="application/json" id="class-average-data">{{ average|safe }}</script>
         <script>
 

x_sys/settings.py

@@ -87,11 +87,11 @@ WSGI_APPLICATION = 'x_sys.wsgi.application'
 DATABASES = {
     'default': {
         'ENGINE': 'django.db.backends.postgresql',
-        'NAME': 'webdefender',
+        'NAME': 'xsysdb',
-        'USER': 'defenderuser',
+        'USER': 'tech4biz',
         'PASSWORD': 'Admin@123',
-        'HOST':'localhost',
+        'HOST': 'localhost',
-        'PORT': '5432',
+        'PORT': '5433',
         
     }
 }