THE ROLE OF DEEP LEARNING IN CYBER DECEPTION TECHNIQUES FOR NETWORK DEFENSE

Abstract

In the ever-evolving landscape of cybersecurity, the efficacy of traditional defense mechanisms against sophisticated threats is increasingly questioned. Cyber deception, a strategy rooted in deliberately misleading adversaries, has gained prominence as a proactive defense approach. Concurrently, deep learning, a subset of artificial intelligence, has emerged as a potent tool for analyzing vast data sets and detecting intricate patterns. This paper explores the fusion of deep learning and cyber deception techniques for network defense. Drawing from historical contexts and contemporary cybersecurity challenges, we investigate how deep learning enhances deception strategies, thereby augmenting network defense capabilities. Through an analysis of case studies, we illustrate the practical application of deep learning in cyber deception scenarios. Additionally, we address challenges such as data scarcity, interpretability issues, and adversarial attacks, while proposing future research avenues. This study provides insights into the synergy between deep learning and cyber deception, offering valuable perspectives for advancing network defense strategies in the digital age.