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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 = []
# Example vulnerability detection logic
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 = []
# Example graph-based detection logic
G = nx.Graph()
G.add_node(target)
# Add more nodes and edges based on the target's network
# Example: G.add_edge(node1, node2)
# Analyze the graph for vulnerabilities
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 = []
# Example integration with other security tools
# Placeholder for integration logic
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 = []
# Example Hak5 vulnerability detection logic
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)
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