Towards Innovative Intelligent Cyber Defense Approach Based on Deceptive Security and Deep Learning Techniques
- Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (LNICST) , LNICST, 713 (1) : 69-87
Résumé
In this paper, we propose a critical analysis of recent advances in cybersecurity, particularly the application of deep learning, cyber threat intelligence, and deceptive security. Building on this analysis, we introduce an integrated and innovative approach that combines the collection of attacker traces through deception systems (honeypots) with deep learning techniques, in order to enhance the resilience of cyber defense strategies. Our methodology is structured around three components: (1) constructing a labeled dataset from the traces collected on the T-Pot deception platform, based on the MITRE ATT&CK framework; (2) developing a deep learning model capable of identifying attacker profiles in real time; and (3) integrating an automated incident response mechanism that can trigger countermeasures tailored to the identified profile. This approach seeks to overcome the limitations of current solutions by providing an adaptive, intelligent and automated cyber defense system.
Mots-clés
Cybersecurity, Deceptive Security, Deep Learning, Real Time