def visualize_results(df, priority_scores, feature_importance):
fig, axes = plt.subplots(2, 3, figsize=(18, 10))
fig.suptitle('Vulnerability Scanner - ML Analysis Dashboard', fontsize=16, fontweight="bold")
axes(0, 0).hist(priority_scores, bins=30, color="crimson", alpha=0.7, edgecolor="black")
axes(0, 0).set_xlabel('Priority Score')
axes(0, 0).set_ylabel('Frequency')
axes(0, 0).set_title('Priority Score Distribution')
axes(0, 0).axvline(np.percentile(priority_scores, 75), color="orange", linestyle="--", label="75th percentile")
axes(0, 0).legend()
axes(0, 1).scatter(df('cvss_score'), priority_scores, alpha=0.6, c=priority_scores, cmap='RdYlGn_r', s=50)
axes(0, 1).set_xlabel('CVSS Score')
axes(0, 1).set_ylabel('ML Priority Score')
axes(0, 1).set_title('CVSS vs ML Priority')
axes(0, 1).plot((0, 10), (0, 1), 'k--', alpha=0.3)
severity_counts = df('severity').value_counts()
colors = {'CRITICAL': 'darkred', 'HIGH': 'red', 'MEDIUM': 'orange', 'LOW': 'yellow'}
axes(0, 2).bar(severity_counts.index, severity_counts.values, color=(colors.get(s, 'gray') for s in severity_counts.index))
axes(0, 2).set_xlabel('Severity')
axes(0, 2).set_ylabel('Count')
axes(0, 2).set_title('Severity Distribution')
axes(0, 2).tick_params(axis="x", rotation=45)
top_features = feature_importance.head(10)
axes(1, 0).barh(top_features('feature'), top_features('importance'), color="steelblue")
axes(1, 0).set_xlabel('Importance')
axes(1, 0).set_title('Top 10 Feature Importance')
axes(1, 0).invert_yaxis()
if 'cluster' in df.columns:
cluster_counts = df('cluster').value_counts().sort_index()
axes(1, 1).bar(cluster_counts.index, cluster_counts.values, color="teal", alpha=0.7)
axes(1, 1).set_xlabel('Cluster')
axes(1, 1).set_ylabel('Count')
axes(1, 1).set_title('Vulnerability Clusters')
attack_vector_counts = df('attack_vector').value_counts()
axes(1, 2).pie(attack_vector_counts.values, labels=attack_vector_counts.index, autopct="%1.1f%%", startangle=90)
axes(1, 2).set_title('Attack Vector Distribution')
plt.tight_layout()
plt.show()
def main():
print("="*70)
print("AI-ASSISTED VULNERABILITY SCANNER WITH ML PRIORITIZATION")
print("="*70)
print()
fetcher = CVEDataFetcher()
df = fetcher.fetch_recent_cves(days=30, max_results=50)
print(f"Dataset Overview:")
print(f" Total CVEs: {len(df)}")
print(f" Date Range: {df('published').min()(:10)} to {df('published').max()(:10)}")
print(f" Severity Breakdown: {df('severity').value_counts().to_dict()}")
print()
feature_extractor = VulnerabilityFeatureExtractor()
embeddings = feature_extractor.extract_semantic_features(df('description').tolist())
df = feature_extractor.extract_keyword_features(df)
df = feature_extractor.encode_categorical_features(df)
prioritizer = VulnerabilityPrioritizer()
X = prioritizer.prepare_features(df, embeddings)
severity_map = {'LOW': 0, 'MEDIUM': 1, 'HIGH': 2, 'CRITICAL': 3, 'UNKNOWN': 1}
y_severity = df('severity').map(severity_map).values
y_score = df('cvss_score').values
X_scaled = prioritizer.train_models(X, y_severity, y_score)
priority_scores, severity_probs, score_preds = prioritizer.predict_priority(X)
df('ml_priority_score') = priority_scores
df('predicted_score') = score_preds
analyzer = VulnerabilityAnalyzer(n_clusters=5)
clusters = analyzer.cluster_vulnerabilities(embeddings)
df = analyzer.analyze_clusters(df, clusters)
feature_imp, emb_imp = prioritizer.get_feature_importance()
print(f"n--- Feature Importance ---")
print(feature_imp.head(10))
print(f"nAverage embedding importance: {emb_imp:.4f}")
print("n" + "="*70)
print("TOP 10 PRIORITY VULNERABILITIES")
print("="*70)
top_vulns = df.nlargest(10, 'ml_priority_score')(('cve_id', 'cvss_score', 'ml_priority_score', 'severity', 'description'))
for idx, row in top_vulns.iterrows():
print(f"n{row('cve_id')} (Priority: {row('ml_priority_score'):.3f})")
print(f" CVSS: {row('cvss_score'):.1f} | Severity: {row('severity')}")
print(f" {row('description')(:100)}...")
print("nnGenerating visualizations...")
visualize_results(df, priority_scores, feature_imp)
print("n" + "="*70)
print("ANALYSIS COMPLETE")
print("="*70)
print(f"nResults summary:")
print(f" High Priority (>0.7): {(priority_scores > 0.7).sum()} vulnerabilities")
print(f" Medium Priority (0.4-0.7): {((priority_scores >= 0.4) & (priority_scores <= 0.7)).sum()}")
print(f" Low Priority (<0.4): {(priority_scores < 0.4).sum()}")
return df, prioritizer, analyzer
if __name__ == "__main__":
results_df, prioritizer, analyzer = main()
print("n✓ All analyses completed successfully!")
print("nYou can now:")
print(" - Access results via 'results_df' DataFrame")
print(" - Use 'prioritizer' to predict new vulnerabilities")
print(" - Explore 'analyzer' for clustering insights")
