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 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 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 benign
traffic and prevalent real-world attacks, providing a comprehensive foundation
for our research.