A Comparative Study of Deep Learning Techniques for Network Intrusion Detection

In an era dominated by interconnected technologies, the persistent threat of intrusion attacks looms ominously, leaving individuals and organizations vulnerable to devastating consequences. The insidious nature of these attacks not only compromises sensitive information but also jeopardizes the very fabric of digital trust. As the frequency and sophistication of intrusions escalate, there is an urgent need for robust defenses to safeguard against these malicious incursions. This paper addresses this pressing concern by delving into the realm of deep learning, a cutting-edge field that holds the promise of fortifying our digital fortresses. We present a comprehensive analysis of various deep learning approaches, rigorously tested on two benchmark datasets: UNSW-NB15 and 5G-NIDD. These datasets serve as battlegrounds where our models are trained and tested to discern their efficacy in the critical task of intrusion detection.