Leveraging Bayesian Multinomial Models for Effective Targeted Cyber-Deception Strategies
Nazia Sharmin, Christopher D. Kiekintveld · 2024
Cyber deception strategies play a vital role in fortifying computer networks and systems against malicious intrusions by misleading attackers. This study presents a novel approach to enhance cyber deception strategies by integrating multinomial Bayesian logistic regression, data interpretation, and feature deception techniques. Our method leverages data-driven learning to analyze uncertainty in the data, effectively disguising one asset as another. We propose a deception framework aimed at increasing entropy in the recognition of a target by employing data learning in cyber deception to mislead attackers and safeguard critical systems and networks. To evaluate our framework’s performance, we employ various statistical measures and assess its predictive accuracy. Our deception results illustrate a hindrance in achieving high predictive accuracy and underscore the targeted deception strategies facilitated by our approach.