## 3 Methodology
```
1 importrequests
2 importtensorflowastf
3 fromtensorflow.keras.modelsimportSequential
4 fromtensorflow.keras.layersimportDense,Conv2D,Flatten,Dropout
5
6 Functiontretrievedatafromtheblockchain
7 defget_blockchain_data():
8#ReplacewiththeactualAPIendpoint
9 url='http://your-blockchain-node/api/vpn-data'
10 response=requests.get(url)
11 ifresponse.status.code=2200:
12#Processandreturndata
13
14 returnprocess_data(response.json())
15 else:
16 returnNone
17
18#Placeholderfordataprocessingfunction
19
20 defprocess_data(data):
21
22#ConvertingthedatatoCNNinputformate
23 returnprocessed_data
24
25#DefiningCNNmodel
26 defcreate_cnn_model(input_shape):
27 model=Sequential(1)
28 model.add(Conv2D32,kernel_size=(3,3),activation='relu',input_shape=input_shape))
29 model.add(Conv2D64,(3,3),activation='relu'))
30 model.add(Flatten())
31 model.add(Dense(128,activation='relu'))
32 model.add(Dropout(0.5))
33 model.add(Dense(1,activation='sigmoid'))#ormoreneuronsformulti-class
34
35 model.compile(loss='binary_crossentropy',#or'categorical_crossentropy'formulti-class
36 optimizer='adam',
37 metrics='accuracy']
38 returnmodel
39
40#Mainfunctiontorestateddataretrievalandanalysis
41 defmain():
42 data=get_blockchain_data()
43 ifdataisnotNone:
44
45#dataisalreadysplitintofeaturesandlabels
46X_train,y_train=data!features'],data['labels']
47 input_shape=X_train.shape[1:]#Shapeoftheinputdata
48 model=create_cnn_model(input_shape)
49 model.fit(X_train,y_train,epochs=10,batch_size=64)
50
51#Addevaluationandpredictionstepsasneeded
52
53
54#EvaluatethemodelonesetVVNforensicsdata
55X_test,y_test=data!test_features'],data['test_labels']
56 evaluation=model.evaluate(X_test,y_test)
57 print(f^TestLoss:[evaluation[0]],TestAccuracy:[evaluation[1]]\")
58
59 predictions=model.predict(X_test)
60 predicted_classes=[1 ifp>0.5 else0 forpinpredictions]
61
62
63#Comparepredicted_classeswithy_testtofurtheranalyzemodelperformance
64
65
66
67 if_name=='_main_':
68 main()
```

**Data Retrieval from Blockchain:** The function 'get_blockchain_data' is responsible for fetching VPN forensics data from the blockchain. It uses the'requests' library to make an HTTP GET request to a specified API endpoint. This endpoint connects to a Hyperledger Fabric node which hosts the VPN data. If the request is successful (status code 200), the data are received in JSON format and then passed to the 'process_data' function for processing. **Data Processing**: The 'process_data' function, currently a placeholder, is intended to convert the raw blockchain data into a format suitable for CNN analysis. This step is crucial as it involves transforming the data into a format that the CNN can efficiently process, such as normalizing values, reshaping arrays, or extracting relevant features. **CNN Model Definition**: The 'create_cnn_model' function sets up the CNN model for data analysis. The model consists of convolutional layers ('Conv2D') for feature extraction, followed by a flattening layer ('Flatten') and dense layers ('Dense') for classification. A dropout layer ('Dropout') is also included to reduce overfitting. The model is compiled with a loss function and optimizer suitable for binary or multi-class classification. **Model Training and Analysis**: In the'main' function, the VPN data are retrieved and processed. The CNN model is then instantiated with the appropriate input shape and trained on the prepared data ('X_train', 'y_train'). This training process involves the model learning to classify or predict outcomes based on the VPN forensics data. Utilizing a Convolutional Neural Network (CNN) for VPN forensics data analysis can yield a diverse range of classifications, pivotal for cybersecurity insights. This advanced analysis can differentiate between normal and anomalous VPN traffic patterns, detect irregularities in user behavior, and identify potential security breaches, such as unauthorized access or data exfiltration. The CNN's ability to discern geolocation anomalies and scrutinize protocol usage further aids in pinpointing non-compliant or risky activities.