Leveraging Computational Intelligence Techniques for Defensive Deception: A Review, Recent Advances, Open Problems and Future Directions

Abstract

With information systems worldwide being attacked daily, analogies from traditional warfare are apt, and deception tactics have historically proven effective as both a strategy and a technique for Defense. Defensive Deception includes thinking like an attacker and determining the best strategy to counter common attack strategies. Defensive Deception tactics are beneficial at introducing uncertainty for adversaries, increasing their learning costs, and, as a result, lowering the likelihood of successful attacks. In cybersecurity, honeypots and honeytokens and camouflaging and moving target defense commonly employ Defensive Deception tactics. For a variety of purposes, deceptive and anti-deceptive technologies have been created. However, there is a critical need for a broad, comprehensive and quantitative framework that can help us deploy advanced deception technologies. Computational intelligence provides an appropriate set of tools for creating advanced deception frameworks. Computational intelligence comprises two significant families of artificial intelligence technologies: deep learning and machine learning. These strategies can be used in various situations in Defensive Deception technologies. This survey focuses on Defensive Deception tactics deployed using the help of deep learning and machine learning algorithms. Prior work has yielded insights, lessons, and limitations presented in this study. It culminates with a discussion about future directions, which helps address the important gaps in present Defensive Deception research.

A green scheduling algorithm for cloud-based honeynets

“…Researchers and experts can analyze the patterns and actions [14,2] by engineering such systems to be intentionally vulnerable. As a deceptive technologies, both honeypots and honeynets rest upon a rich literature and have been extensively mapped in regards to critical semantics [1,15,16].…”

“…Dissimilar to intelligent honeypots and honeynets however, learning-based systems examine behavior within the user environment instead of network connections [29,30]. In other words, the system learns what might maximize sojourn time and alters itself to influence the metric towards a theoretical maximal value [31,16]. Strikingly, the majority of effort has gone into integrating reinforcement learning as the underlying ML technique.…”

AI-driven container security approaches for 5G and beyond: A survey

“…whether they are supervised, semi-supervised or unsupervised. A review conducted by Mohan et al [7] focused on the applications of various ML and deep learning methods in the implementation of defensive deception. They summarized the classi ication of several deception categories, new machine learning and deep learning techniques in defensive deception, including the models, common datasets, key contributions, and limitations.…”

IoDM: A Study on a IoT-Based Organizational Deception Modeling with Adaptive General-Sum Game Competition

“…As a lightweight counter-measure to mitigate the spatiotemporal defender inferiority issue of IoT systems, and to protect and defend IoT systems against potential advanced persistent threat (APT) attacks, defensive cyber deception techniques [6][7][8] were introduced. Defensive cyber deception techniques are non-cooperative decision-making pollution techniques that mislead potential attackers' cognitive perspectives, deceiving the attackers…”