CYBER SECURITY ATTACK DETECTION MODEL USING SEMI-SUPERVISED LEARNING

Abstract

The increasing digitalization of our society has brought about numerous benefits, enabling seamless communication, convenient transactions, and efficient operations. However, with this growing reliance on interconnected systems and information technology, the risk of cyber-attacks has also surged. Cyber threats, such as data breaches, ransomware, and sophisticated malware, have become more prevalent, threatening the confidentiality, integrity, and availability of critical data and services. Organizations across industries face the daunting challenge of defending against a wide array of cyber-attacks that continue to evolve in complexity and stealth. In response to this ever-changing cyber threat landscape, Cyber Security Risk Management (CSRM) and attack detection have become critical components of any comprehensive cybersecurity strategy. The ability to identify and mitigate cyber risks and swiftly detect malicious activities is paramount for safeguarding sensitive information, preserving business continuity, and maintaining the trust of customers and stakeholders. A novel approach to Cyber Security Risk Management through an Attack Detection Model that utilizes Semi-Supervised Learning Auto-Encoders in conjunction with Probabilistic Bayesian Networks. The study compares the performance of Multi Connect Variational Auto-Encoder (MC-VAE), Probabilistic Bayesian Networks (PBN), and a combined model of MC-VAE and PBN. The study employs the NUSW-NB15_GT dataset for training and evaluation purposes. Notably, the Semi-Supervised Learning with Probabilistic Bayesian Networks (SSL-PBN) model demonstrates exceptional results, achieving a precision rate of 94% and a recall rate of 90%. The F1 score of 0.9191 highlights the SSL-PBN model's efficacy in achieving a balanced trade-off between precision and recall, critical for minimizing false positives and false negatives...