Zero-day Network Intrusion Detection using Machine Learning Approach

Abstract

Zero-day network attacks are a growing global cybersecurity concern. Hackers exploit vulnerabilities in network systems, making network traffic analysis crucial in detecting and mitigating unauthorized attacks. However, inadequate and ineffective network traffic analysis can lead to prolonged network compromises. To address this, machine learning-based zero-day network intrusion detection systems (ZDNIDS) rely on monitoring and collecting relevant information from network traffic data. The selection of pertinent features is essential for optimal ZDNIDS performance given the voluminous nature of network traffic data, characterized by attributes. Unfortunately, current machine learning models utilized in this field exhibit inefficiency in detecting zero-day network attacks, resulting in a high false alarm rate and overall performance degradation. To overcome these limitations, this paper introduces a novel approach combining the anomaly-based extended isolation forest algorithm with the BAT algorithm and Nevergrad. Furthermore, the proposed model was evaluated using 5G network traffic, showcasing its effectiveness in efficiently detecting both known and unknown attacks, thereby reducing false alarms when compared to existing systems. This advancement contributes to improved internet security.