Deceptive Cybersecurity Defense: Neuro-Adaptive Honeypots Using Deep Neural Networks

Abstract

In the dynamic environment of current threats, the need for intelligent defense measures is more important than ever. Such an approach, called honeypots, blindly guides them by providing fake but highly valuable targets. But traditional honeypots remain passive and originated for slow-growing and obsolete threats, not for modern dynamic threats. This work introduces the incorporation of deep neural networks (DNNs) into honeypots, which gives rise to neuro-adaptive honeypots. These intelligent systems fully utilize machine learning in order to adapt autonomously and are therefore becoming more and more efficient at eluding attackers and trapping them as well. These honeypots are actually neuro-adaptive in that they gain experience from the activities of the attackers in real-time and are thus likely to deceive and, therefore, detect the attackers. This paper aims to analyze neuro-adaptive honeypots, particularly the use of DNNs at the core of the system design and operation. Furthermore, we outline the advantages, risks, and future applications of the presented approach to enhance cybersecurity defense mechanisms against rather more powerful and concealed cyber threats.