Sarcouncil Journal of Applied Sciences Aims & Scope

Abstract: The rapid evolution of cybersecurity threats and the growing complexity of digital infrastructures have necessitated innovative approaches to product security management. This study explores the potential of machine learning (ML)-powered penetration testing (pentesting) as a novel solution to enhance vulnerability detection, risk prioritization, and workflow efficiency. By leveraging advanced ML algorithms, the research demonstrates significant improvements over traditional pentesting methods, achieving a 92.5% overall vulnerability detection rate compared to 78.3% for traditional approaches. Key findings include a reduction in false positives (4.2% vs. 8.7%) and false negatives (3.8% vs. 10.5%), as well as a 45% reduction in scan time and optimized resource utilization. ML-powered pentesting also excelled in risk prioritization, with precision and recall for high-risk vulnerabilities reaching 91.3% and 88.7%, respectively. Furthermore, the integration of ML-powered tools into existing workflows resulted in a 35% reduction in manual effort and a 28% increase in efficiency. Validation tests confirmed the robustness and generalizability of the ML models, with cross-environment accuracy averaging 89.7%. These findings highlight the transformative potential of ML-powered pentesting in addressing modern cybersecurity challenges, offering a scalable, accurate, and efficient approach to product security management. The study concludes with recommendations for future research, including the integration of ML with emerging technologies and the development of open-source tools to broaden accessibility