Explainable Artificial Intelligence Applications in Cyber Security
Savitha B, Achyutha Prasad N, B K. Sushmitha, R Megha, Laxmi Laxmi, LeenaShruthi Hm · 2024
In the rapidly evolving landscape of cybersecurity, the identification and relief of cyber-attacks have become paramount to protect sensitive information and maintain system integrity. This paper explores an innovative approach to cyber-attack detection within a Whitebox environment, utilizing cutting-edge machine learning techniques and comprehensive system monitoring techniques. Our proposed methodology integrates deep learning models with utilizing instantaneous data examination to recognize and address anomalies indicative of malicious activity. By employing a Whitebox testing strategy, we gain complete visibility into the system's internal states, enabling more accurate and timely detection of sophisticated attacks. The experiment's Findings indicate that our strategy considerably improves rates of detection while lowering false positives compared to traditional Blackbox methods. This study advances the area of cybersecurity by presenting a scalable yet efficient approach for real-time cyber-attacks detection, emphasising the need of transparency and thorough monitoring in defending digital infrastructures.