Detecting Targeted Attacks by Multilayer Deception

Over the past few years, enterprises are facing a growing number of highly customized and targeted attacks that use sophisticated techniques and seek after important company assets, such as customer data and intellectual property. Unlike conventional attacks, targeted attacks are operated by experts who use multiple steps to gain access to sensitive assets, and most of time, leave very few network traces behind for detection. In this paper, we propose a multi-layer deception system that provides an in depth defense against such sophisticated targeted attacks. Specifically, based on previous knowledge and patterns of such attacks, we model the attacker as trying to compromising an enterprise network via multiple stages of penetration and propose defenses at each of these layers using deception based detection. Due to multiple layers of deception, the probability of detecting such an attack will be greatly enhanced. We present a proof of concept implementation of one of the key deception methods proposed. Due to various financial constraints of an enterprise, we also model the design of the deception system as an optimization problem in order to minimize the total expected loss due to system deployment and asset compromise. We find that there is an optimal solution to deploy deception entities, and even over spending budget on more entities will only increase the total expected loss to the enterprise. Such a system