Visible to the public Blind Attack Flaws in Adaptive Honeypot Strategies

TitleBlind Attack Flaws in Adaptive Honeypot Strategies
Publication TypeConference Paper
Year of Publication2021
AuthorsObaidat, Muath, Brown, Joseph, Alnusair, Awny
Conference Name2021 IEEE World AI IoT Congress (AIIoT)
KeywordsAdaptation models, Adaptive, Adaptive systems, artificial intelligence, deception, Fingerprint recognition, honey pots, honeypot, human factors, Network, Optimized production technology, pubcrawl, resilience, Resiliency, Scalability, security, Threat
AbstractAdaptive honeypots are being widely proposed as a more powerful alternative to the traditional honeypot model. Just as with typical honeypots, however, one of the most important concerns of an adaptive honeypot is environment deception in order to make sure an adversary cannot fingerprint the honeypot. The threat of fingerprinting hints at a greater underlying concern, however; this being that honeypots are only effective because an adversary does not know that the environment on which they are operating is a honeypot. What has not been widely discussed in the context of adaptive honeypots is that they actually have an inherently increased level of susceptibility to this threat. Honeypots not only bear increased risks when an adversary knows they are a honeypot rather than a native system, but they are only effective as adaptable entities if one does not know that the honeypot environment they are operating on is adaptive as wekk. Thus, if adaptive honeypots become commonplace - or, instead, if attackers even have an inkling that an adaptive honeypot may exist on any given network, a new attack which could develop is a "blind confusion attack"; a form of connection which simply makes an assumption all environments are adaptive honeypots, and instead of attempting to perform a malicious strike on a given entity, opts to perform non-malicious behavior in specified and/or random patterns to confuse an adaptive network's learning.
DOI10.1109/AIIoT52608.2021.9454206
Citation Keyobaidat_blind_2021