Stochastic Model for Cognitive Radio Networks under Jamming Attacks and Honeypot-Based Prevention

Bhunia, Suman; Su, Xing; Sengupta, Shamik; Vázquez-Abad, Felisa J.

doi:10.1007/978-3-642-45249-9_29

Cited by 16 publications

(9 citation statements)

References 10 publications

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“…In our previous work [19], we introduced the concept of honeynet in CRN and presented the benefits of dedicating one SU as honeynode in a multichannel CRN provided the honeynode is successful of enticing the attacker. We presented a stochastic model for honeynode selection which proved that in the case of uniform traffic, selecting a SU with lowest queue size is optimal in terms of overall system performance.…”

Section: Background Studiesmentioning

confidence: 99%

CR-Honeynet: A Learning &amp; Decoy Based Sustenance Mechanism against Jamming Attack in CRN

Bhunia

Sengupta

Vázquez-Abad

2014

2014 IEEE Military Communications Conference

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Cognitive Radio Network (CRN) enables secondary users to borrow unused spectrum from the proprietary users in a dynamic and opportunistic manner. However, dynamic and open access nature of available spectrum brings a serious challenge of sustenance amongst CRNs which makes them vulnerable to various spectrum etiquette attacks. Jamming-based denial of service (DoS) attack poses serious threats to legitimate communications and packet delivery. A rational attacker targets certain transmission characteristics to find the highest impacting communication of CRN and causes maximum disruption. In this paper, inspired by the honeypot concept in cybercrime, we propose a honeynet based defense mechanism, which aims to deter the attacker from jamming legitimate communications. The honeynet passively learns the attacker's strategy from the past history of attacks and actively adapts preemptive decoy mechanisms to prevent attacks on legitimate communications. Simulation results show that the with help of honeynet mechanism, CRN successfully avoids jamming attacks and thereby improves system performance in terms of packet delivery ratio.

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Section: Background Studiesmentioning

confidence: 99%

CR-Honeynet: A Learning &amp; Decoy Based Sustenance Mechanism against Jamming Attack in CRN

Bhunia

Sengupta

Vázquez-Abad

2014

2014 IEEE Military Communications Conference

Self Cite

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“…It exploits the time and spatial diversity of the CRs to deal with collaborative jamming attack in an infrastructure-based centralised CRN. Furthermore, the concept of honeynode has been shown in Bhunia et al (2014) to be effective in deceiving jammers about the transmitting nodes. In this reference, a single honeynode is dynamically selected for each transmitting period, to act as a normal transmitting CR in order to attract the jammer to a specific channel.…”

Section: Deceptive/reactive Jammingmentioning

confidence: 99%

A modified Q-learning algorithm to solve cognitive radio jamming attack

Slimeni¹,

Scheers²,

Chtourou³

et al. 2018

IJES

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Contents:IJES is a refereed international journal providing an international forum to report, discuss and exchange experimental or theoretical results, novel designs, work-in-progress, experience, case studies, and trend-setting ideas. Papers should be of a quality that represents the latest advances in embedded systems in time-to-market, cost, code size, weight, testability, power, real-time behaviour, and stimulating future trends.

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“…It exploits the time and spatial diversity of the CRs to deal with collaborative jamming attack in an infrastructure based centralized CRN. Furthermore, the concept of honeynode has been shown in [8] to be effective in deceiving jammers about the transmitting nodes. In this reference, a single honeynode is dynamically selected for each transmitting period, to act as a normal transmitting CR in order to attract the jammer to a specific channel.…”

Section: ) Deceptive/reactive Jammingmentioning

confidence: 99%

Jamming mitigation in cognitive radio networks using a modified Q-learning algorithm

Slimeni

Scheers

Chtourou

et al. 2015

2015 International Conference on Military Communications and Information Systems (ICMCIS)

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The jamming attack is one of the most severe threats in cognitive radio networks, because it can lead to network degradation and even denial of service. However, a cognitive radio can exploit its ability of dynamic spectrum access and its learning capabilities to avoid jammed channels. In this paper, we study how Q-learning can be used to learn the jammer strategy in order to pro-actively avoid jammed channels. The problem with Q-learning is that it needs a long training period to learn the behavior of the jammer. To address the above concern, we take advantage of the wideband spectrum sensing capabilities of the cognitive radio to speed up the learning process and we make advantage of the already learned information to minimize the number of collisions with the jammer during training. The effectiveness of this modified algorithm is evaluated by simulations in the presence of different jamming strategies and the simulation results are compared to the original Q-learning algorithm applied to the same scenarios.

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Stochastic Model for Cognitive Radio Networks under Jamming Attacks and Honeypot-Based Prevention

Cited by 16 publications

References 10 publications

CR-Honeynet: A Learning &amp; Decoy Based Sustenance Mechanism against Jamming Attack in CRN

CR-Honeynet: A Learning &amp; Decoy Based Sustenance Mechanism against Jamming Attack in CRN

A modified Q-learning algorithm to solve cognitive radio jamming attack

Jamming mitigation in cognitive radio networks using a modified Q-learning algorithm

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Stochastic Model for Cognitive Radio Networks under Jamming Attacks and Honeypot-Based Prevention

Cited by 16 publications

References 10 publications

CR-Honeynet: A Learning &amp;amp; Decoy Based Sustenance Mechanism against Jamming Attack in CRN

CR-Honeynet: A Learning &amp;amp; Decoy Based Sustenance Mechanism against Jamming Attack in CRN

A modified Q-learning algorithm to solve cognitive radio jamming attack

Jamming mitigation in cognitive radio networks using a modified Q-learning algorithm

Contact Info

Product

Resources

About

CR-Honeynet: A Learning & Decoy Based Sustenance Mechanism against Jamming Attack in CRN

CR-Honeynet: A Learning & Decoy Based Sustenance Mechanism against Jamming Attack in CRN