Deep Learning-Based System for Detecting Network Intrusions

Abstract

The rapid growth of interconnected and interoperable computing environments has become a vital component in supporting everyday activities. However, this interconnectivity simultaneously introduces exploitable weaknesses that extend beyond the limits of human oversight. Such weaknesses highlight the critical need for cybersecurity mechanisms to safeguard digital communication. Ensuring secure information exchange demands protective strategies that can resist present threats while also adapting to emerging ones. This study presents a deep learning–based framework for building a dynamic and robust network intrusion detection system (IDS) capable of identifying and categorizing malicious traffic. The focus lies on how deep neural networks (DNNs) can empower IDS solutions with adaptive learning abilities, enabling the detection of both known attacks and previously unseen, zero-day behaviors. This adaptability assists in expelling intruders and lowering the probability of system compromise. To validate the effectiveness of the proposed approach, experiments were conducted using the UNSW-NB15 dataset, which mirrors realistic modern network interactions combined with artificially generated attack scenarios.