PRELIMINARY STUDY ON ARTIFICIAL INTELLIGENCE METHODS FOR CYBERSECURITY THREAT DETECTION INCOMPUTER NETWORKS BASED ON RAWDATA PACKETS

Abstract

Most of the intrusion detection methods in computer networks are based ontraffic flow characteristics. However, this approach may not fully exploit thepotential of deep learning algorithms to directly extract features and patternsfrom raw packets. Moreover, it impedes real-time monitoring due to the neces-sity of waiting for the processing pipeline to complete and introduces depen-dencies on additional software components.In this paper, we investigate deep learning methodologies capable of de-tecting attacks in real-time directly from raw packet data within network traffic.Our investigation utilizes the CIC IDS-2017 dataset, which includes both benigntraffic and prevalent real-world attacks, providing a comprehensive foundationfor our research