| python | | | # ai_pen_tester.py | | | import torch | | | import torch.nn as nn | | | import scapy.all as scapy | | | import requests | | | import nmap | | | # Define the AI model architecture | |
| class PenTestModel(nn.Module): | |
| def __init__(self): | |
| super(PenTestModel, self).__init__() | |
| self.fc1 = nn.Linear(128, 128) # input layer (128) -> hidden layer (128) | |
| self.fc2 = nn.Linear(128, 128) # hidden layer (128) -> hidden layer (128) | |
| self.fc3 = nn.Linear(128, 2) # hidden layer (128) -> output layer (2) | |
| def forward(self, x): | |
| x = torch.relu(self.fc1(x)) | |
| x = torch.relu(self.fc2(x)) | |
| x = self.fc3(x) | |
| return x | | | # Define the penetration testing functions | | | class PenTester: | | | def init(self, model): | | | self.model = model | | | def scan_network(self, ip_range): | | | # Use scapy to perform a basic network scan | |
| arp_request = scapy.ARP(pdst=ip_range) | |
| broadcast = scapy.Ether(dst="ff:ff:ff:ff:ff:ff") | |
| arp_request_broadcast = broadcast/arp_request | | | answered_list = scapy.srp(arp_request_broadcast, timeout=1, verbose=False)[0] | | | # Create a list of IP addresses to scan | | | ip_addresses = [answered_list[i][1].psrc for i in range(len(answered_list))] | | | return ip_addresses | | | def scan_web_app(self, ip_address): | | | # Use requests to perform a basic web application scan | |
| url = f"http://{ip_address}" | |
| response = requests.get(url) | |
| # Check for common web application vulnerabilities | | | if response.status_code == 200: | | | # Check for SQL injection vulnerabilities | | | sql_injection_url = f"{url}/?id=1' OR '1' = '1" | | | sql_injection_response = requests.get(sql_injection_url) | | | if sql_injection_response.status_code == 200: | | | return True | | | # Check for cross-site scripting (XSS) vulnerabilities | |
| xss_url = f"{url}/?name=<script>alert('XSS')</script>" | |
| xss_response = requests.get(xss_url) | |
| if xss_response.status_code == 200: | |
| return True | | | return False | | | def run_pen_test(self, ip_range): | | | # Scan the network and identify potential targets | | | ip_addresses = self.scan_network(ip_range) | | | # Use the AI model to predict which targets are most likely to be vulnerable | | | predictions = [] | | | for ip_address in ip_addresses: | | | # Create a feature vector for the target | |
| features = [1, 0, 1, 0] # placeholder features | |
| features = torch.tensor(features, dtype=torch.float32) | |
| # Run the feature vector through the AI model | |
| output = self.model(features) | |
| prediction = torch.argmax(output) | |
| # Add the prediction to the list | | | predictions.append((ip_address, prediction)) | | | # Sort the predictions by confidence | | | predictions.sort(key=lambda x: x[1], reverse=True) | | | # Run the penetration test on the top N targets | |
| for ip_address, _ in predictions[:5]: | |
| if self.scan_web_app(ip_address): | |
| print(f"Vulnerability found on {ip_address}!") | |
| # Create an instance of the AI model and the penetration tester | |
| model = PenTestModel() | |
| pen_tester = PenTester(model) | |
| # Run the penetration test | | | pen_tester.run_pen_test("192.168.1.0/24") |