Hybrid intrusion detection system in fog computing

This paper presents a Hybrid Intrusion Detection System (HIDS) that integrates both signature-based and anomaly-based detection methods to enhance network security tailored for fog computing environments. The system leverages the strengths of signature-based detection for identifying known threats while employing anomaly-based techniques to detect novel and evolving attacks. By utilising deep learning and machine learning models, the HIDS ensures low-latency processing, high detection accuracy, and reduced false positives, addressing the specific security challenges of distributed fog networks. The system’s architecture, implementation details, and performance evaluation are discussed, demonstrating its effectiveness in identifying and mitigating various cyber threats.