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Found 8 papers, 0 papers with code
ZETAR: Modeling and Computational Design of Strategic and Adaptive Compliance Policies
Incentive design is a proactive and non-invasive approach to achieving compliance by aligning an insider's incentive with the defender's security objective, which motivates (rather than commands) an insider to act in the organization's interests.
RADAMS: Resilient and Adaptive Alert and Attention Management Strategy against Informational Denial-of-Service (IDoS) Attacks
In this work, we identify and formally define a new type of proactive attentional attacks called Informational Denial-of-Service (IDoS) attacks that generate a large volume of feint attacks to overload human operators and hide real attacks among feints.
Combating Informational Denial-of-Service (IDoS) Attacks: Modeling and Mitigation of Attentional Human Vulnerability
The numerical results illustrate how AM strategies can alleviate the severity level and the risk of IDoS attacks.
Reinforcement Learning for Feedback-Enabled Cyber Resilience
In this work, we review the literature on RL for cyber resilience and discuss cyber resilience against three major types of vulnerabilities, i. e., posture-related, information-related, and human-related vulnerabilities.
ADVERT: An Adaptive and Data-Driven Attention Enhancement Mechanism for Phishing Prevention
The results show that the visual aids can statistically increase the attention level and improve the accuracy of phishing recognition from 74. 6% to a minimum of 86%.
Strategic Learning for Active, Adaptive, and Autonomous Cyber Defense
The increasing instances of advanced attacks call for a new defense paradigm that is active, autonomous, and adaptive, named as the \texttt{`3A'} defense paradigm.
Cryptography and Security
A Dynamic Game Framework for Rational and Persistent Robot Deception With an Application to Deceptive Pursuit-Evasion
The deception is rational as robots aim to achieve their deception goals at minimum cost.
Adaptive Honeypot Engagement through Reinforcement Learning of Semi-Markov Decision Processes
In this work, we apply infinite-horizon Semi-Markov Decision Process (SMDP) to characterize a stochastic transition and sojourn time of attackers in the honeynet and quantify the reward-risk trade-off.
