Detection of Zero-Day Attacks using CNN and LSTM in Networked Autonomous Systems 'IEEE CNS 23 Poster'

Hassan El Alami, Danda B. Rawat

Abstract: In this paper, we propose a novel approach for detecting zero-day attacks on networked autonomous systems (AS). The proposed approach combines CNN and LSTM algorithms to offer efficient and precise detection of zero-day attacks. We evaluated the proposed approach’s performance against various ML models using a real-world dataset. The experimental results demonstrate the effectiveness of the proposed approach in detecting zero-day attacks in networked AS, achieving better accuracy and detection probability than other ML models.