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Cyber threats, such as advanced persistent threats (APTs), ransomware, andzero-day exploits, are rapidly evolving and demand improved security measures.Honeypots and honeynets, as deceptive systems, offer valuable insights intoattacker behavior, helping researchers and practitioners develop innovativedefense strategies and enhance detection mechanisms. However, their deploymentinvolves significant maintenance and overhead expenses. At the same time, thecomplexity of modern computing has prompted the rise of autonomic computing,aiming for systems that can operate without human intervention. Recent honeypotand honeynet research claims to incorporate autonomic computing principles,often using terms like adaptive, dynamic, intelligent, and learning. This studyinvestigates such claims by measuring the extent to which autonomic principlesprinciples are expressed in honeypot and honeynet literature. The findingsreveal that autonomic computing keywords are present in the literature sample,suggesting an evolution from self-adaptation to autonomic computingimplementations. Yet, despite these findings, the analysis also shows lowfrequencies of self-configuration, self-healing, and self-protection keywords.Interestingly, self-optimization appeared prominently in the literature. Whilethis study presents a foundation for the convergence of autonomic computing anddeceptive systems, future research could explore technical implementations insample articles and test them for autonomic behavior. Additionally,investigations into the design and implementation of individual autonomiccomputing principles in honeypots and determining the necessary ratio of theseprinciples for a system to exhibit autonomic behavior could provide valuableinsights for both researchers and practitioners.

To What Extent Are Honeypots and Honeynets Autonomic Computing Systems?