Recent research in cybersecurity has begun to develop active defense strategies using game‐theoretic optimization of the allocation of limited defenses combined with deceptive signaling. These algorithms assume rational human behavior. However, human behavior in an online game designed to simulate an insider attack scenario shows that humans, playing the role of attackers, attack far more often than predicted under perfect rationality. We describe an instance‐based learning cognitive model, built in ACT‐R, that accurately predicts human performance and biases in the game. To improve defenses, we propose an adaptive method of signaling that uses the cognitive model to trace an individual's experience in real time. We discuss the results and implications of this adaptive signaling method for personalized defense.

This work is an initial step toward developing a cognitive theory of cyber deception. While widely studied, the psychology of deception has largely focused on physical cues of deception. Given that present-day communication among humans is largely electronic, we focus on the cyber domain where physical cues are unavailable and for which there is less psychological research. To improve cyber defense, researchers have used signaling theory to extended algorithms developed for the optimal allocation of limited defense resources by using deceptive signals to trick the human mind. However, the algorithms are designed to protect against adversaries that make perfectly rational decisions. In behavioral experiments using an abstract cybersecurity game (i.e., Insider Attack Game), we examined human decision-making when paired against the defense algorithm. We developed an instance-based learning (IBL) model of an attacker using the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture to investigate how humans make decisions under deception in cyber-attack scenarios. Our results show that the defense algorithm is more effective at reducing the probability of attack and protecting assets when using deceptive signaling, compared to no signaling, but is less effective than predicted against a perfectly rational adversary. Also, the IBL model replicates human attack decisions accurately. The IBL model shows how human decisions arise from experience, and how memory retrieval dynamics can give rise to cognitive biases, such as confirmation bias. The implications of these findings are discussed in the perspective of informing theories of deception and designing more effective signaling schemes that consider human bounded rationality.

This paper improves upon recent game-theoretic deceptive signaling schemes for cyber defense using the insights emerging from a cognitive model of human cognition. One particular defense allocation algorithm that uses a deceptive signaling scheme is the peSSE (Xu et al., 2015). However, this static signaling scheme optimizes the rate of deception for perfectly rational adversaries and is not personalized to individuals. Here we advance this research by developing a dynamic and personalized signaling scheme using cognitive modeling. A cognitive model based on a theory of experiential-choice (Instance-Based Learning Theory; IBLT), implemented in a cognitive architecture (Adaptive Control of Thought -Rational; ACT-R), and validated using human experimentation with deceptive signals informs the development of a cognitive signaling scheme. The predictions of the cognitive model show that the proposed solution increases the compliance to deceptive signals beyond the peSSE. These predictions were verified in human experiments, and the results shed additional light on human reactions towards adaptive deceptive signals.

Synaesthesia has previously been linked with imagery abilities, although an understanding of a causal role for mental imagery in broader synaesthetic experiences remains elusive. This can be partly attributed to our relatively poor understanding of imagery in sensory domains beyond vision. Investigations into the neural and behavioural underpinnings of mental imagery have nevertheless identified an important role for imagery in perception, particularly in mediating cross-modal interactions. However, the phenomenology of synaesthesia gives rise to the assumption that associated cross-modal interactions may be encapsulated and specific to synaesthesia. As such, evidence for a link between imagery and perception may not generalize to synaesthesia. Here, we present results that challenge this idea: first, we found enhanced somatosensory imagery evoked by visual stimuli of body parts in mirror-touch synaesthetes, relative to other synaesthetes or controls. Moreover, this enhanced imagery generalized to tactile object properties not directly linked to their synaesthetic associations. Second, we report evidence that concurrent experience evoked in grapheme—colour synaesthesia was sufficient to trigger visual-to-tactile correspondences that are common to all. Together, these findings show that enhanced mental imagery is a consistent hallmark of synaesthesia, and suggest the intriguing possibility that imagery may facilitate the cross-modal interactions that underpin synaesthesic experiences. This article is part of a discussion meeting issue ‘Bridging senses: novel insights from synaesthesia’.

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