| import logging |
| import networkx as nx |
| from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, VotingClassifier |
| from sklearn.model_selection import train_test_split |
| from sklearn.metrics import accuracy_score |
| from sklearn.preprocessing import StandardScaler |
|
|
| class VulnerabilityScanner: |
| def __init__(self): |
| self.scan_results = [] |
| self.rf_model = RandomForestClassifier(n_estimators=100) |
| self.gb_model = GradientBoostingClassifier(n_estimators=100) |
| self.ensemble_model = VotingClassifier(estimators=[('rf', self.rf_model), ('gb', self.gb_model)], voting='soft') |
| self.data = None |
| self.labels = None |
| self.scaler = StandardScaler() |
|
|
| def scan(self, target): |
| logging.info(f"Scanning target: {target}") |
| vulnerabilities = self.detect_vulnerabilities(target) |
| graph_based_vulnerabilities = self.graph_based_detection(target) |
| integrated_vulnerabilities = self.integrate_with_security_tools(target) |
| all_vulnerabilities = vulnerabilities + graph_based_vulnerabilities + integrated_vulnerabilities |
| self.scan_results.append({ |
| "target": target, |
| "vulnerabilities": all_vulnerabilities |
| }) |
| return all_vulnerabilities |
|
|
| def detect_vulnerabilities(self, target): |
| logging.info(f"Detecting vulnerabilities for target: {target}") |
| vulnerabilities = [] |
| |
| if "example.com" in target: |
| vulnerabilities.append({"id": "CVE-2021-1234", "description": "Sample vulnerability 1", "severity": "High"}) |
| if "test.com" in target: |
| vulnerabilities.append({"id": "CVE-2021-5678", "description": "Sample vulnerability 2", "severity": "Medium"}) |
| return vulnerabilities |
|
|
| def graph_based_detection(self, target): |
| logging.info(f"Performing graph-based detection for target: {target}") |
| vulnerabilities = [] |
| |
| G = nx.Graph() |
| G.add_node(target) |
| |
| |
| |
| if G.has_node(target): |
| vulnerabilities.append({"id": "CVE-2022-0003", "description": "Graph-based vulnerability 1", "severity": "High"}) |
| return vulnerabilities |
|
|
| def integrate_with_security_tools(self, target): |
| logging.info(f"Integrating with other security tools for target: {target}") |
| vulnerabilities = [] |
| |
| |
| if "securitytool.com" in target: |
| vulnerabilities.append({"id": "CVE-2022-0004", "description": "Security tool vulnerability 1", "severity": "High"}) |
| return vulnerabilities |
|
|
| def scan_hak5_vulnerabilities(self, target): |
| logging.info(f"Scanning Hak5 vulnerabilities for target: {target}") |
| vulnerabilities = [] |
| |
| if "hak5.com" in target: |
| vulnerabilities.append({"id": "CVE-2022-0001", "description": "Hak5 vulnerability 1", "severity": "High"}) |
| if "ducky.com" in target: |
| vulnerabilities.append({"id": "CVE-2022-0002", "description": "Hak5 vulnerability 2", "severity": "Medium"}) |
| self.scan_results.append({ |
| "target": target, |
| "vulnerabilities": vulnerabilities |
| }) |
| return vulnerabilities |
|
|
| def render(self): |
| return "Vulnerability Scanner Module: Ready to scan and report vulnerabilities." |
|
|
| def integrate_with_new_components(self, new_component_data): |
| logging.info("Integrating with new components") |
| integrated_data = { |
| "new_component_vulnerabilities": new_component_data.get("vulnerabilities", {}) |
| } |
| self.scan_results.append(integrated_data) |
| return self.scan_results |
|
|
| def ensure_compatibility(self, existing_data, new_component_data): |
| logging.info("Ensuring compatibility with existing vulnerability scanner logic") |
| compatible_data = { |
| "existing_vulnerabilities": existing_data.get("vulnerabilities", {}), |
| "new_component_vulnerabilities": new_component_data.get("vulnerabilities", {}) |
| } |
| return compatible_data |
|
|
| def preprocess_data(self, data): |
| return self.scaler.fit_transform(data) |
|
|
| def load_data(self, data, labels): |
| self.data = self.preprocess_data(data) |
| self.labels = labels |
|
|
| def train_model(self): |
| if self.data is None or self.labels is None: |
| raise ValueError("Data and labels must be loaded before training the model.") |
| |
| X_train, X_test, y_train, y_test = train_test_split(self.data, self.labels, test_size=0.2, random_state=42) |
| self.ensemble_model.fit(X_train, y_train) |
| predictions = self.ensemble_model.predict(X_test) |
| accuracy = accuracy_score(y_test, predictions) |
| return accuracy |
|
|
| def predict(self, new_data): |
| if self.ensemble_model is None: |
| raise ValueError("Model must be trained before making predictions.") |
| |
| preprocessed_data = self.preprocess_data(new_data) |
| return self.ensemble_model.predict(preprocessed_data) |
|
|
