Defensive deception is a promising approach for cyber defense. Via defensive deception, a defender can anticipate and prevent attacks by misleading or luring an attacker, or hiding some of its resources. Although defensive deception is garnering increasing research attention, there has not been a systematic investigation of its key components, the underlying principles, and its tradeoffs in various problem settings. This survey focuses on defensive deception research centered on game theory and machine learning, since these are prominent families of artificial intelligence approaches that are widely employed in defensive deception. This paper brings forth insights, lessons, and limitations from prior work. It closes with an outline of some research directions to tackle major gaps in current defensive deception research.

Cyber deception aims to misrepresent the state of the network to mislead the attackers, falsify their reconnaissance conclusions, and deflect them away from their goals. Honeypots serve as decoy devices inside networks that can capture adversaries for monitoring purposes. We propose a two-phase deception approach based on honeypot allocation. In the first phase, we develop a proactive deceptive honeypot allocation policy, the second phase proposes a reactive deception approach that dynamically allocates honeypots according to IDS updates. Considering a practical scenario, the defender partially monitors the adversary’s activities. To this end, we develop our deception approach using a combination of game-theoretic and reinforcement learning models. We cast the problem of reactive deception as a partially observable Markov decision process (POMDP) based on a game-theoretic dynamic model to accommodate the imperfect monitoring of the actions taken by the attacker. We solve this combined partially observable game model using Monte-Carlo tree search to overcome the game model complexity. We give a game-theoretic analysis to explain the attack-defense policies at equilibrium. Finally, we present numerical results to validate the effectiveness of the proposed deception approach.

Defensive deception techniques have emerged as a promising proactive defense mechanism to mislead an attacker and thereby achieve attack failure. However, most game-theoretic defensive deception approaches have assumed that players maintain consistent views under uncertainty. They do not consider players’ possible, subjective beliefs formed due to asymmetric information given to them. In this work, we formulate a hypergame between an attacker and a defender where they can interpret the same game differently and accordingly choose their best strategy based on their respective beliefs. This gives a chance for defensive deception strategies to manipulate an attacker’s belief, which is the key to the attacker’s decision-making. We consider advanced persistent threat (APT) attacks, which perform multiple attacks in the stages of the cyber kill chain (CKC) where both the attacker and the defender aim to select optimal strategies based on their beliefs. Through extensive simulation experiments, we demonstrated how effectively the defender can leverage defensive deception techniques while dealing with multi-staged APT attacks in a hypergame in which the imperfect information is reflected based on perceived uncertainty, cost, and expected utilities of both the attacker and defender, the system lifetime (i.e., mean time to security failure), and improved false-positive rates of intrusion detection.

Defensive deception techniques have emerged as a promising proactive defense mechanism to mislead an attacker and thereby achieve attack failure. However, most game-theoretic defensive deception approaches have assumed that players maintain consistent views under uncertainty. They do not consider players’ possible, subjective beliefs formed due to asymmetric information given to them. In this work, we formulate a hypergame between an attacker and a defender where they can interpret the same game differently and accordingly choose their best strategy based on their respective beliefs. This gives a chance for defensive deception strategies to manipulate an attacker’s belief, which is the key to the attacker’s decision-making. We consider advanced persistent threat (APT) attacks, which perform multiple attacks in the stages of the cyber kill chain (CKC) where both the attacker and the defender aim to select optimal strategies based on their beliefs. Through extensive simulation experiments, we demonstrated how effectively the defender can leverage defensive deception techniques while dealing with multi-staged APT attacks in a hypergame in which the imperfect information is reflected based on perceived uncertainty, cost, and expected utilities of both the attacker and defender, the system lifetime (i.e., mean time to security failure), and improved false-positive rates of intrusion detection.