Ethical Hacking and Penetration Testing: Strengthening Cyber Defense with AI-Driven Breach and Attack Simulation

Abstract

Ethical hacking and penetration testing are essential for identifying and mitigating security vulnerabilities in an era of increasing cyber threats. Traditional penetration testing methods, while effective, face challenges such as high costs, time-consuming assessments, and limited adaptability to evolving attack patterns. To address these limitations, this study proposes an AI-Driven Breach and Attack Simulation framework that integrates machine learning algorithms and automation techniques to simulate real-world attacks, analyze system weaknesses, and provide actionable security insights in real time. By leveraging Graph Neural Networks (GNN) for attack path prediction, BERT-based models for vulnerability detection, and Security Orchestration, Automation, and Response for risk assessment, the AI-driven Breach and Attack Simulation (BAS) system ensures continuous security evaluation while reducing false positives and response time. Experimental results demonstrate the superiority of AI-driven BAS over conventional penetration testing methods, achieving improved detection accuracy, automated remediation, and enhanced cyber resilience. This research highlights the importance of AI-powered cybersecurity solutions in proactively defending against modern cyber threats, suggesting that AI-driven BAS can revolutionize penetration testing by providing scalable, real-time, and adaptive security measures across various industries.