Cyberattacks are proliferating, and deception via honeypots may provide efficient strategies for combating cyberattacks. Although prior research has examined deception and network factors using deception-based games, it is still unknown how the proportion of honeypots in a network influences the adversarial decision. This study evaluates the influence of different honeypot proportions on the adversary’s decisions using a deception game (DG). DG has two consecutive stages, probe and attack. In the probe stage, participants may probe a few webservers or not probe the network. In the attack stage, participants may attack any of the webservers or decide not to attack the webservers. Participants were randomly assigned to one of three between-subject conditions containing different honeypot proportions: small, medium, and large. With an increase in the proportion of honeypots, the honeypot and no-attack actions increased dramatically. We show how our findings are applicable in deception-based cyber scenarios.
Prior research in judgment and decision making (JDM) has investigated the effect of problem framing on human preferences. Furthermore, research in JDM documented the absence of such reversal of preferences when making decisions from experience. However, little is known about the effect of context on preferences under the combined influence of problem framing and problem format. Also, little is known about how cognitive models would account for human choices in different problem frames and types (general/specific) in the experience format. One of the primary objectives of this research is to investigate the presence of preference reversals under the influence of problem framing (gain/loss), problem format (experience/description), and problem type (general/specific). Another objective of this research is to develop cognitive models to account for human choices across different problem frames and types in the experience format. A total of 320 participants from India were randomly assigned to one of eight between‐subjects conditions that differed in problem frame, format, and type. Results revealed preference reversals in the description condition; however, they were absent in the experience condition. Moreover, preference reversals were less pronounced in the general problem framing compared to the specific problem framing. Furthermore, specific problems influenced risk‐seeking behavior among participants. We developed cognitive and heuristics models using instance‐based learning theory and natural mean heuristic. Results reveal models’ dependency on recent and frequent observations during information sampling. These experience‐based cognitive models could help build artificial intelligence models with fewer preference reversals.
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