Preliminary study on artificial intelligence methods for cybersecurity threat detection in computer networks based on raw data packets
This addresses real-time intrusion detection for computer networks, though it appears incremental as it adapts existing computer vision methods to a new data representation.
The paper tackles the problem of real-time cybersecurity threat detection by proposing a deep learning approach that processes raw network packets directly as 2D images, achieving detection on the CIC IDS-2017 dataset.
Most of the intrusion detection methods in computer networks are based on traffic flow characteristics. However, this approach may not fully exploit the potential of deep learning algorithms to directly extract features and patterns from raw packets. Moreover, it impedes real-time monitoring due to the necessity of waiting for the processing pipeline to complete and introduces dependencies on additional software components. In this paper, we investigate deep learning methodologies capable of detecting attacks in real-time directly from raw packet data within network traffic. We propose a novel approach where packets are stacked into windows and separately recognised, with a 2D image representation suitable for processing with computer vision models. Our investigation utilizes the CIC IDS-2017 dataset, which includes both benign traffic and prevalent real-world attacks, providing a comprehensive foundation for our research.