A High-Performance Hybrid Entropy Complexity Learning Framework for Robust Detection of Stealthy Cyber Attacks

Abstract

Cyber-attacks are advancing toward secretive behaviors that avoid classical signature detection systems and single-metric abnormality approaches. This paper is used to be proposed a framework-based Hybrid Entropy–Complexity Learning (HECL) for strong detection of cyber-attacks based on networking information models. The proposed approach cooperatively analyzes the entropy-based Shannon scheme, statistical complexity, and learning adaptability for characterizing normal and abnormal traffic activities. Different from conventional systems-based detection entropy, the proposed method utilizes the temporal coupling between both the entropy and complexity, allowing the correspondence of low-rate and whitewash attacks. Comprehensive simulations illustrate that the proposed model performs outstanding detection accuracy, speedy convergence, and a lower rate-based false-alarm when it is compared to the standalone entropy and approaches-based Machine Learning (ML). The key results emphasize that integrating an information-theoretic system with the learning-based indicators gives an influential and lightweight security technique appropriate for recent information security systems. The experimental results on the CICIDS2017 dataset show that it outperforms the state of the art deep learning techniques with 98.85% accuracy and 0.95% false positive rate.