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1. Deception through Half-Truths

   https://doi.org/10.1609/aaai.v34i06.6570  

   Estornell, Andrew; Das, Sanmay; Vorobeychik, Yevgeniy (June 2020, Proceedings of the AAAI Conference on Artificial Intelligence)

   Deception is a fundamental issue across a diverse array of settings, from cybersecurity, where decoys (e.g., honeypots) are an important tool, to politics that can feature politically motivated “leaks” and fake news about candidates. Typical considerations of deception view it as providing false information. However, just as important but less frequently studied is a more tacit form where information is strategically hidden or leaked. We consider the problem of how much an adversary can affect a principal's decision by “half-truths”, that is, by masking or hiding bits of information, when the principal is oblivious to the presence of the adversary. The principal's problem can be modeled as one of predicting future states of variables in a dynamic Bayes network, and we show that, while theoretically the principal's decisions can be made arbitrarily bad, the optimal attack is NP-hard to approximate, even under strong assumptions favoring the attacker. However, we also describe an important special case where the dependency of future states on past states is additive, in which we can efficiently compute an approximately optimal attack. Moreover, in networks with a linear transition function we can solve the problem optimally in polynomial time. 

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2. On Mitigation of Active Eavesdropping Attack by Spoofing Relay

   Tugnait, Jitendra K (June 2017, 2017 IEEE 85th Vehicular Technology Conference: VTC2017-Spring)

   We consider detection of spoofing relay attack in time-division duplex (TDD) multiple antenna systems where an adversary operating in a full-duplex mode, amplifies and forwards the training signal of the legitimate receiver. In TDD systems, the channel state information (CSI) can be acquired using reverse training. The spoofing relay attack contaminates the channel estimation phase. Consequently the beamformer designed using the contaminated channel estimate can lead to a significant information leakage to the attacking adversary. A recent approach proposed using the minimum description length (MDL) criterion to detect spoofing relay attack. In this paper we augment this approach with joint channel estimation and secure beamforming to mitigate the effects of pilot contamination by spoofing relay. The proposed mitigation approach is illustrated via simulations. 

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3. Decepticon: A Hidden Markov Model Approach to Counter Advanced Persistent Threats

   Baksi, Rudraprasad; Upadhyaya, Shambhu (December 2019, Secure Knowledge Management In Artificial Intelligence Era (SKM 2019))

   Deception has been proposed in the literature as an effective defense mechanism to address Advanced Persistent Threats (APT). However, administering deception in a cost-effective manner requires a good understanding of the attack landscape. The attacks mounted by APT groups are highly diverse and sophisticated in nature and can render traditional signature based intrusion detection systems useless. This necessitates the development of behavior oriented defense mechanisms. In this paper, we develop Decepticon (Deception-based countermeasure) a Hidden Markov Model based framework where the indicators of compromise (IoC) are used as the observable features to aid in detection. This framework would help in selecting an appropriate deception script when faced with APTs or other similar malware and trigger an appropriate defensive response. The effectiveness of the model and the associated framework is demonstrated by considering ransomware as the offending APT in a networked system. 

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4. A Strategic Framework for Mitigating Advanced Persistent Threats: A Hidden Markov Model Approach

   Baksi, R.; Upadhyaya, S. (July 2019, 2nd Conference on Risk Analysis, Decision Analysis and Security, Niagara Falls, NY)

   Deception has been proposed in the literature as an effective defense mechanism to address Advanced Persistent Threats (APT). However, administering deception in a cost-effective manner requires a good understanding of the attack landscape. In this paper, we develop a Hidden Markov Model based framework where the indicators of compromise (IoC) are used as the observables. This framework would help in selecting an appropriate deception script and triggering the proper defensive strategy when faced with APTs or other malware. The effectiveness of the model and the associated framework are illustrated by considering ransomware as the offending APT in a networked system. 

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5. Stochastic Dueling Bandits with Adversarial Corruption

   Agarwal, Arpit; Agarwal, Shivani; Patil, Prathamesh (January 2021, Proceedings of the 32nd International Conference on Algorithmic Learning Theory)

   The dueling bandits problem has received a lot of attention in recent years due to its applications in recommendation systems and information retrieval. However, due to the prevalence of malicious users in these systems, it is becoming increasingly important to design dueling bandit algorithms that are robust to corruptions introduced by these malicious users. In this paper we study dueling bandits in the presence of an adversary that can corrupt some of the feedback received by the learner. We propose an algorithm for this problem that is agnostic to the amount of corruption introduced by the adversary: its regret degrades gracefully with the amount of corruption, and in case of no corruption, it essentially matches the optimal regret bounds achievable in the purely stochastic dueling bandits setting. 

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