Miniproject

[SayeedaFatimaJabeen]

import pandas as pd from sklearn.ensemble import RandomForestClassifier from sklearn.preprocessing import LabelEncoder from cryptography.fernet import Fernet from datetime import datetime

Step 1: Define Sample Data

data = { 'user_id': [1, 2, 3, 4, 5], 'role': ['doctor', 'nurse', 'admin', 'doctor', 'admin'], 'patient_id': [101, 102, 103, 101, 104], 'access_time': ['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-04', '2023-01-05'], 'action': ['view', 'modify', 'view', 'modify', 'view'], 'data': ['encrypted_data_1', 'encrypted_data_2', 'encrypted_data_3', 'encrypted_data_1', 'encrypted_data_4'] }

df = pd.DataFrame(data) df['access_time'] = pd.to_datetime(df['access_time'])

Step 2: Machine Learning-Based Access Control

Train a simple classifier for access control

le = LabelEncoder() df['role_encoded'] = le.fit_transform(df['role'])

X = df[['user_id', 'role_encoded']] y = df['action']

classifier = RandomForestClassifier() classifier.fit(X, y)

Rest of the code remains the same...

Step 3: Machine Learning-Based Encryption

For simplicity, use a symmetric encryption scheme (Fernet)

key = Fernet.generate_key() cipher_suite = Fernet(key)

Encrypt sensitive data

df['encrypted_data'] = df['data'].apply(lambda x: cipher_suite.encrypt(x.encode()).decode())

Step 4: Comprehensive Audit Trail

Log audit trail events

audit_log = []

def log_event(user_id, role, patient_id, action, access_time): audit_log.append({ 'user_id': user_id, 'role': role, 'patient_id': patient_id, 'action': action, 'access_time': access_time })

Simulate access events

for index, row in df.iterrows(): user_id = row['user_id'] role = row['role'] patient_id = row['patient_id'] action = row['action'] access_time = row['access_time']

# Step 5: Access Control Check
if classifier.predict([[user_id, le.transform([role])[0]]]) == action:
    # Authorized access
    log_event(user_id, role, patient_id, action, access_time)
else:
    # Unauthorized access
    print(f"Unauthorized access detected: User {user_id} with role {role} attempted {action} on patient {patient_id}")

Step 6: Print Audit Log

print("Audit Log:") print(pd.DataFrame(audit_log))

Step 7: Print Decrypted Data

(For demonstration purposes; in a real scenario, decryption would happen only for authorized users)

df['decrypted_data'] = df['encrypted_data'].apply(lambda x: cipher_suite.decrypt(x.encode()).decode()) print("Decrypted Data:") print(df[['user_id', 'patient_id', 'decrypted_data']])