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

Franco, Javier; Arış, Ahmet; Canberk, Berk; Uluagac, A. Selcuk

doi:10.1109/comst.2021.3106669

Cited by 141 publications

(63 citation statements)

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“…Several studies have investigated the role of honeypots and relevant optimisation techniques with AI and game theory in order to protect critical organisations and infrastructures. Some of them are listed below [12], [28]- [34]. In particular, in [28], J. Franco et al provide a survey about honeypots and honeynets for the IoT and IIoT.…”

Section: Related Workmentioning

confidence: 99%

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Strategic Honeypot Deployment in Ultra-Dense Beyond 5G Networks: A Reinforcement Learning Approach

Radoglou-Grammatikis

Sarigiannidis

Diamantoulakis

et al. 2024

IEEE Trans. Emerg. Topics Comput.

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The progression of Software Defined Networking (SDN) and the virtualisation technologies lead to the beyond 5G era, providing multiple benefits in the smart economies. However, despite the advantages, security issues still remain. In particular, SDN/NFV and cloud/edge computing are related to various security issues. Moreover, due to the wireless nature of the entities, they are prone to a wide range of cyberthreats. Therefore, the presence of appropriate intrusion detection mechanisms is critical. Although both Machine Learning (ML) and Deep Learning (DL) have optimised the typical rule-based detection systems, the use of ML and DL requires labelled pre-existing datasets. However, this kind of data varies based on the nature of the respective environment. Another smart solution for detecting intrusions is to use honeypots. A honeypot acts as a decoy with the goal to mislead the cyberatatcker and protect the real assets. In this paper, we focus on Wireless Honeypots (WHs) in ultradense networks. In particular, we introduce a strategic honeypot deployment method, using two Reinforcement Learning (RL) techniques: (a) e − Greedy and (b) Q − Learning. Both methods aim to identify the optimal number of honeypots that can be deployed for protecting the actual entities. The experimental results demonstrate the efficacy of both methods.

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Section: Related Workmentioning

confidence: 99%

“…Some of them are listed below [12], [28]- [34]. In particular, in [28], J. Franco et al provide a survey about honeypots and honeynets for the IoT and IIoT. In [12], M. Nawrocki et al present a comprehensive study about honeypot software and relevant data analytics.…”

Section: Related Workmentioning

confidence: 99%

Strategic Honeypot Deployment in Ultra-Dense Beyond 5G Networks: A Reinforcement Learning Approach

Radoglou-Grammatikis

Sarigiannidis

Diamantoulakis

et al. 2024

IEEE Trans. Emerg. Topics Comput.

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“…In 2020, Lee et al [37] The authors [39] suggested: development of Honeypot systems for effective insider attack detection and mitigation; investigating technologies, platforms, and domains recently becoming prominent; investigating established but yet unexplored protocols; investigating effective deployment locations and remote management; and also, effective anti-Honeypot and ant-detection approaches.…”

Section: Survey Reviewmentioning

confidence: 99%

Meta-Review of Recent and Landmark Honeypot Research and Surveys

Ikuomenisan¹,

Morgan²

2022

JIS

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The growing interest in Honeypots has resulted in increased research, and consequently, a large number of research surveys and/or reviews. Most Honeypot surveys and/or reviews focus on specific and narrow Honeypot research areas. This study aims at exploring and presenting advances and trends in Honeypot's research and development areas. To this end, a systematic methodology and meta-review analysis were applied to the selection, evaluation, and qualitative examination of the most influential Honeypot surveys and/or reviews available in scientific bibliographic databases. A total of 188 papers have been evaluated and 22 research papers are found by this study to have a higher impact. The findings of the study suggest that the Honeypot survey and/or review papers of considerable relevance to the research community were mostly published in 2018, by IEEE, in conferences organized in India, and included in the IEEE Xplore database. Also, there have been few qualities Honeypot surveys and/or reviews published after 2018. Furthermore, the study identified 10 classes of vital and emerging themes and/or key topics in Honeypot research. This work contributes to research efforts employing established systematic review and reporting methods in Honeypot research. We have included our meta-review methodology, in order to allow further work in this area aiming at a better understanding of the progression of Honeypot research and advances.

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“…HONEYPOT As the applications of ICS -in particular-are increasing so do the attacks and as we mentioned earlier ICS are uses in critical industries some are managed by the federal agencies of the US and most of them are privately owned [5], hence, in order to protect theise critical systems from attackers, we can deploy different security aspects such as IDS, IPS, Firewalls, cryptography, antivirus and other tools can be applied to secure the system. But on the other hand these tools or mechanisms can't offer a chance to study how the attackers can get into the system, how they act, what is the attack flow, and also the identity of the attackers [1]. However, here comes Honeypot and Honeynet, a system that traps and tricks the attackers into believing that they have got access to the actual assets of the system while they just got into a simulated system that registers their attack patterns.…”

Section: IIImentioning

confidence: 99%

“…classification by application: Honeypot is a tool created to be applied and used within IoT systems such as IoT candyjar honeypot and IoTPOT honeypot, or for industrial purposes with ICS such as Honeyd+ honeypot and CamouflageNet honeynet [1].…”

Section: Amentioning

confidence: 99%

Improving ICS security through Honeynets and Machine Learning techniques

Ananbeh

Alomari

Daniell

2022

Preprint

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The internet of things(IoT), the Industrial Internet of Things (IIoT), and Cyber-Physical Systems (CPS) can be seen everywhere, Home applications, Buildings, Cars, Space Industry, Military, Health Care, and in many other fields. On the other hand, they become an easier target for attackers, due to many reasons including the limitation of hardware, so from that point, companies start working to build a secure systems by keep themselves updated about their system threats and vulnerabilities, and also by studying how the attackers can gets into their system, how they act, what is the attack flow, and also the identity of the attackers by trapping and tricking them into believing that they have got access to the actual system or assets . And that’s what it's called a Honeypot. [1] As the technology keeps changing and becomes more powerful, so do the attackers, and for that reason companies should use new techniques to enhance Honeypots efficacy by making it undetectable by cybercriminals, more usable and make use of the information that the honeypots gather in a more efficient way. Moreover, Machine Learning (ML) techniques are able to provide intelligence to IoT, IIoT, and ICS systems and networks, and enhance its ability to deal with various security problems, hence, in this research, we are developing a new solution that improves the architecture of SCADA (An ICS System) by adding CamouflageNet Honeynet into it and ML techniques, in order to defend and acquisition system security performance.

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

Cited by 141 publications

References 100 publications

Strategic Honeypot Deployment in Ultra-Dense Beyond 5G Networks: A Reinforcement Learning Approach

Strategic Honeypot Deployment in Ultra-Dense Beyond 5G Networks: A Reinforcement Learning Approach

Meta-Review of Recent and Landmark Honeypot Research and Surveys

Improving ICS security through Honeynets and Machine Learning techniques

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