On the rewards of self-adaptive IoT honeypots

Pauna, Adrian; Bica, Ion; Pop, Florin; Castiglione, Aniello

doi:10.1007/s12243-018-0695-7

Cited by 16 publications

(14 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Discussions of specific parts of RL solution design problems occur in smaller number of cases, but these kinds of publication demonstrate the fact that constructing an appropriate RL application is not always trivial. We can highlight state space design [12,25,33,107,144,179,193,208,217,220,222,224,227,266,267] and action space design [109,220,246,268], reward construction [14,76,110,199,220,226,246,[269][270][271][272][273], and exploration strategy planning [86,274] which can be determinants from the whole application point of view. [11,13,17,20,21,24,38,43,61,62,66,69,82,89,…”

Section: Complexitymentioning

confidence: 99%

The Applicability of Reinforcement Learning Methods in the Development of Industry 4.0 Applications

2021

View full text Add to dashboard Cite

Reinforcement learning (RL) methods can successfully solve complex optimization problems. Our article gives a systematic overview of major types of RL methods, their applications at the field of Industry 4.0 solutions, and it provides methodological guidelines to determine the right approach that can be fitted better to the different problems, and moreover, it can be a point of reference for R&D projects and further researches.

show abstract

Section: Complexitymentioning

confidence: 99%

The Applicability of Reinforcement Learning Methods in the Development of Industry 4.0 Applications

2021

View full text Add to dashboard Cite

show abstract

“…Honeypots and related technologies, e.g., honeyfiles, honeynet, honeybot, etc., have been widely used to mitigate stealthy and deceptive attacks. In particular, RL has been widely used to develop self-adaptive honeypots in normal conditions [76,77,78] and resource-constrained environment [79]. As attackers become more sophisticated, they manage to fingerprint and evade honeypots [80].…”

Section: Information-related Vulnerabilitymentioning

confidence: 99%

Reinforcement Learning for Feedback-Enabled Cyber Resilience

Huang¹,

Huang²,

Zhu³

2021

Preprint

View full text Add to dashboard Cite

The rapid growth in the number of devices and their connectivity has enlarged the attack surface and made cyber systems more vulnerable. As attackers become increasingly sophisticated and resourceful, mere reliance on traditional cyber protection, such as intrusion detection, firewalls, and encryption, is insufficient to secure the cyber systems. Cyber resilience provides a new security paradigm that complements inadequate protection with resilience mechanisms. A Cyber-Resilient Mechanism (CRM) adapts to the known or zero-day threats and uncertainties in real-time and strategically responds to them to maintain the critical functions of the cyber systems in the event of successful attacks. Feedback architectures play a pivotal role in enabling the online sensing, reasoning, and actuation process of the CRM. Reinforcement Learning (RL) is an important class of algorithms that epitomize the feedback architectures for cyber resiliency. It allows the CRM to provide dynamic and sequential responses to attacks with limited or without prior knowledge of the environment and the attacker. In this work, we review the literature on RL for cyber resiliency and discuss the cyber-resilient defenses against three major types of vulnerabilities, i.e., posture-related, information-related, and human-related vulnerabilities. We introduce moving target defense, defensive cyber deception, and assistive human security technologies as three application domains of CRMs to elaborate on their designs. The RL algorithms also have vulnerabilities themselves. We explain the major vulnerabilities of RL and present works that develop several attack models, in which the attacks target the rewards, the state observations, and the action commands. We show that the attacker can trick the RL agent into learning a nefarious policy with minimum attacking effort. We discuss the potential defense methods to secure them and find that there is a lack of works focusing on the defensive mechanisms for RL-enabled systems. Finally, we discuss the future challenges of RL for cyber security and resiliency and emerging applications of RL-based CRMs.

show abstract

“…With this data, he applied game-theory and Machine Learning (ML) techniques to develop a self-adaptive SSH honeypot called Adaptive Honeypot Alternative (AHA) [63]. While Wagener does not implement his honeypot in an IoT environment, his honeypot serves as the basis for Pauna's works [49], [56], [57], [64]. Wagener reported that attackers carried out three times more interactions when they were responding to the customized tools of an adaptive honeypot, which shows the important role that adaptive honeypots can play in honeypot research.…”

Section: A General Application Honeypotsmentioning

confidence: 99%

“…Pauna's honeypot resides on the Argos emulator as a guest OS, and utilizes Argos to detect rootkit malware. This research was followed by [56], [57], [64].…”

Section: A General Application Honeypotsmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Honeypots and Honeynets for Internet of Things, Industrial Internet of Things, and Cyber-Physical Systems

Franco¹,

Arış²,

Canberk³

et al. 2021

Preprint

View full text Add to dashboard Cite

The Internet of Things (IoT), the Industrial Internet of Things (IIoT), and Cyber-Physical Systems (CPS) have become essential for our daily lives in contexts such as our homes, buildings, cities, health, transportation, manufacturing, infrastructure, and agriculture. However, they have become popular targets of attacks, due to their inherent limitations which create vulnerabilities. Honeypots and honeynets can prove essential to understand and defend against attacks on IoT, IIoT, and CPS environments by attracting attackers and deceiving them into thinking that they have gained access to the real systems. Honeypots and honeynets can complement other security solutions (i.e., firewalls, Intrusion Detection Systems -IDS) to form a strong defense against malicious entities. This paper provides a comprehensive survey of the research that has been carried out on honeypots and honeynets for IoT, IIoT, and CPS. It provides a taxonomy and extensive analysis of the existing honeypots and honeynets, states key design factors for the state-of-the-art honeypot/honeynet research and outlines open issues for future honeypots and honeynets for IoT, IIoT, and CPS environments.

show abstract

On the rewards of self-adaptive IoT honeypots

Cited by 16 publications

References 32 publications

The Applicability of Reinforcement Learning Methods in the Development of Industry 4.0 Applications

The Applicability of Reinforcement Learning Methods in the Development of Industry 4.0 Applications

Reinforcement Learning for Feedback-Enabled Cyber Resilience

A Survey of Honeypots and Honeynets for Internet of Things, Industrial Internet of Things, and Cyber-Physical Systems

Contact Info

Product

Resources

About