CR-Honeynet: A Cognitive Radio Learning and Decoy-Based Sustenance Mechanism to Avoid Intelligent Jammer

Bhunia, Suman; Miles, Edward W.; Sengupta, Shamik; Vázquez-Abad, Felisa J.

doi:10.1109/tccn.2018.2833549

Cited by 17 publications

(12 citation statements)

References 32 publications

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“…Numerous anti-jamming methods have been proposed in the literature, ranging from frequency hopping [3]- [8] methods that employ techniques such as honeypots to obtain the jammer policy or to harvest the jamming energy [9]- [14]. Frequency hopping methods continuously switch the carrier frequency between different bands and can be performed using strategies such as chaotic frequency hopping [7] or learning-based methods [8].…”

Section: A Related Workmentioning

confidence: 99%

“…Frequency hopping methods continuously switch the carrier frequency between different bands and can be performed using strategies such as chaotic frequency hopping [7] or learning-based methods [8]. The authors in [9] propose an anti-jamming technique that assigns a user among all users as a honeypot to obtain the jammer policy for a wiser jamming mitigation. The work in [10] proposes an antijamming method based on dispersing the data in time frames, and models the impacts of the spectrum changes on the mobile cognitive users' performance in hostile environments.…”

Section: A Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Reinforcement Learning for Deceiving Reactive Jammers in Wireless Networks

Pourranjbar,

Kaddoum,

Ferdowsi

et al. 2021

Preprint

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Conventional anti-jamming methods mostly rely on frequency hopping to hide or escape from jammers. These approaches are not efficient in terms of bandwidth usage and can also result in a high probability of jamming. Different from existing works, in this paper, a novel anti-jamming strategy is proposed based on the idea of deceiving the jammer into attacking a victim channel while maintaining the communications of legitimate users in safe channels. Since the jammer's channel information is not known to the users, an optimal channel selection scheme and a sub-optimal power allocation algorithm are proposed using reinforcement learning (RL). The performance of the proposed anti-jamming technique is evaluated by deriving the statistical lower bound of the total received power (TRP). Analytical results show that, for a given access point, over 50% of the highest achievable TRP, i.e. in the absence of jammers, is achieved for the case of a single user and three frequency channels. Moreover, this value increases with the number of users and available channels. The obtained results are compared with two existing RL based anti-jamming techniques, and a random channel allocation strategy without any jamming attacks. Simulation results show that the proposed anti-jamming method outperforms the compared RL based antijamming methods and the random search method, and yields near optimal achievable TRP.

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Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Reinforcement Learning for Deceiving Reactive Jammers in Wireless Networks

Pourranjbar,

Kaddoum,

Ferdowsi

et al. 2021

Preprint

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“…GRC / USRP was used to test their SDR system. In [19] and [20], the anti-jamming strategy was suggested in various methods and their approach was tested using GRC. In [21], The author has created a mechanism called TREKS to facilitate communication with Spread Spectrum without pre-shared information.…”

Section: Softwarementioning

confidence: 99%

Testing different Channel Estimation Techniques in Real-Time Software Defined Radio Environment

Ponnaluru¹,

Satyanarayana²

2020

IJACSA

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In modern wireless communication to maximize spectral efficiency and to minimize the bit error rate OFDM (Orthogonal frequency-domain multiplexing) is used. OFDM is used broadly in networks using various protocols, including wireless vehicular environment IEEE 802.11p, IEEE 802.16d/e Wireless Metropolitan Area Networks, Long-Term Evolution 3GPP networks and IEEE 802.11a/g/n Wireless Local Area Networks. The main challenges involved when using OFDM for wireless communications are short channel-coherence bandwidth and the narrow coherence time, and both have a major effect on the reliability and latency of data packet communication. These properties increase the difficulty of channel equalization because the channel may change drastically over the period of a single packet. Spectral Temporal Averaging is an enhanced decisiondirected channel equalization technique that improves communication performance (as far as the frame delivery ratio (FDR) and throughput) in typical channel conditions. This paper reports tests of Spectral Temporal Averaging channel equalization in an IEEE 802.11a network, compared with other channel equalization techniques in terms of the FDR in a realtime environment. Herein, a software defined Radio (SDR) platform was used for estimating the channel. This proves that the system can provide over 90% of delivery ratio at 25 db of Signal to Noise Ratio (SNR) for various digital modulation techniques. For this purpose, an experimental setup consisting of software-defined radio, Universal Software Radio Peripheral (USRP) N210 along with wide bandwidth daughter board as hardware and GNU radio is used.

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“…The software-defined radio platform makes it possible to generate such a target attack. A honeynet-based defense for jamming is presented [11]. The honeynet learns the attacker's strategy and adapts the preemptive process.…”

Section: Introductionmentioning

confidence: 99%

Cognitive Radio Jamming Attack Detection Using an Autoencoder for CRIoT Network

Nallarasan

Kottursamy

2021

Wireless Pers Commun

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IoT network-connected devices will be kept on increasing and will cross million, but it is impossible to allocate spectrum for those million and million of the devices. This spectrum scarcity can be handled by incorporating cognitive radio-based dynamic spectrum sharing, which is referred to as Cognitive Radio Internet of Things (CRIoT). But CRIoT sufferers from the Physical layer attack in cognitive radio, which affects the spectrum sensing accuracy and reduces the spectrum utilization. There are various attacks at the physical layer of cognitive radio among jamming attacks resulting in a denial of cognitive radio services and make spectrum underutilization. Continuous jamming can be detected quickly based on time delay on spectrum access, but discrete random jamming detection is challenging. This article proposes an autoencoder deep learning architecture-based jamming attack detection in cognitive radio. The jamming detection problem is modeled as anomaly detection. The autoencoder architecture is used to detect the jammer anomaly of the jammer. The proposed system involves the simulation of a random jamming attack and detecting it at a particular time instant information that may help mitigate the jammer attack .the proposed mechanism able to detect the jammer with 89% of accuracy.

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CR-Honeynet: A Cognitive Radio Learning and Decoy-Based Sustenance Mechanism to Avoid Intelligent Jammer

Cited by 17 publications

References 32 publications

Reinforcement Learning for Deceiving Reactive Jammers in Wireless Networks

Reinforcement Learning for Deceiving Reactive Jammers in Wireless Networks

Testing different Channel Estimation Techniques in Real-Time Software Defined Radio Environment

Cognitive Radio Jamming Attack Detection Using an Autoencoder for CRIoT Network

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