Survey on cognitive anti‐jamming communications

Aref, Mohamed A.; Jayaweera, Sudharman K.; Yepez, Esteban

doi:10.1049/iet-com.2020.0024

Cited by 31 publications

(20 citation statements)

References 115 publications

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“…Game theory is widely used in wireless communications to solve communication problems like resource allocation [22]- [24], packet relaying [5] and anti-jamming communication [8], [10], [25]- [27] , as shown in Fig. 2.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The adaptability of CR could provide intelligence to spec-trum sensing and spectrum decision [5]. On the other hand, the adversary can also manoeuvre the same features intelligently to create more harm to the underlying CR Network (CRN) [8]- [10]. Therefore, ensuring security is of paramount importance to the successful deployment of cognitive radio networks.…”

Section: Introductionmentioning

confidence: 99%

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Anti-Jamming Game to Combat Intelligent Jamming for Cognitive Radio Networks

Ibrahim¹,

Qureshi

et al. 2021

IEEE Access

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Cognitive Radio (CR) provides a promising solution to the spectrum scarcity problem in the dense wireless networks, where the sensing ability of cognitive users helps acquire knowledge of the environment. However, cognitive users are vulnerable to different types of attacks, due to its shared medium. In particular, Jamming is considered as one of the most challenging security threat in CR networks. In jamming, an attacker jams the communication by transmitting a high power noise signal in the vicinity of the targeted node. The jammer could be an intelligent entity that is capable of exploiting the dynamics of the environment. In this work, we provide a machine-learning-based anti-jamming technique for CR networks to avoid a hostile jammer, where both the jamming and anti-jamming processes are formulated based on the Markov game framework. In our framework, secondary users avoid the jammer by maximizing its payoff function using an online, model-free reinforcement learning technique called Q-learning. We consider a realistic mathematical model, where the channel conditions are time varying and differ from one sub-channel to another, as in practical scenarios. Simulation results show that our proposed approach outperforms existing approaches to combat jamming over a wide range of scenarios.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Anti-Jamming Game to Combat Intelligent Jamming for Cognitive Radio Networks

Ibrahim¹,

Qureshi

et al. 2021

IEEE Access

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“…To prevent jamming, the CR's flexibility may provide intelligence in spectrum detection and spectrum decisions [1]. On the other hand, the enemy can proactively use the same attributes to cause more damage to the cognitive radio network (CRN)-based communication system [2,3].…”

Section: Introductionmentioning

confidence: 99%

“…Game theory is widely used in wireless communications to solve many communication problems such as resource allocation [10], packet relaying [1], anti-jamming communication [2,3,11,12]. Very recently, Zhu et al in [13] has presented state of the art survey on game's theoretic defensive deception.…”

Section: Introductionmentioning

confidence: 99%

Entice to Trap: Enhanced Protection against a Rate-Aware Intelligent Jammer in Cognitive Radio Networks

et al. 2022

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Anti-jamming in cognitive radio networks (CRN) is mainly accomplished using machine learning techniques in the domains of frequency, coding, power and rate. Jamming is a major threat to CRN because it can cause severe performance damage such as network isolation, network application interruption and even physical damage to infrastructure simple radio devices. With the improvement in communication technologies, the capabilities of adversaries are also increased. The intelligent jammer knows the rate at which users transmit data, which is based on the attractiveness factor of each user. The higher the data rate for a secondary user, the more attractive it is to the rate-aware jammer. In this paper, we present a dummy user in the network as a honeypot of the jammer to get the jammer’s attention. A new anti-jamming deceiving theoretical method based on rate modifications is introduced to increase the bandwidth efficiency of the entire cognitive radio-based communication system. We employ a defensive anti-jamming deception mechanism of the Pseudo Secondary User (PSU) to as an entice to trap the attacker by providing thus enhanced protection for the rest of the network from the impact of the attacker. Our analytical simulation results show a significant improvement in performance using the proposed solution. The utility of the proposed intelligent anti-jamming algorithm lies in its applications to support the secondary wireless sensor nodes.

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“…In recent years, as more and more private sensitive data are exchanged among mobile devices, communication security issues have received widespread attention, such as jamming attacks [1,2]. In the field of anti-jamming, frequency hopping is a common approach and has attracted widespread attention [3,4].…”

Section: Introductionmentioning

confidence: 99%

A multi‐agent reinforcement learning anti‐jamming method with partially overlapping channels

Zhang

Jia

et al. 2021

IET Communications

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This paper investigates the problem of multi‐user anti‐jamming channel access with partially overlapping channels (POC). Compared with traditional anti‐jamming systems that use non‐overlapping channels, POC improve the spectral efficiency. However, the partial overlap of channels also brings more serious interference. For this, physical distance and channel separation on the interference intensity under partially overlapping channels are first considered and the malicious jamming and interference among users are formulated as a hierarchical binary model. Secondly, to cope with multi‐user decisions under dynamic jamming conditions, the Markov game framework is adopted to analyse the problem. Thirdly, a multi‐user collaborative anti‐jamming channel selection algorithm based on reinforcement learning is proposed as well as the optimal anti‐jamming strategy can be obtained. Finally, the simulation results validate that the proposed algorithm helps users cope with jamming and eliminate mutual interference. Compared with the non‐overlapping channel access scheme, the POC access scheme achieves higher network throughput.

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Survey on cognitive anti‐jamming communications

Cited by 31 publications

References 115 publications

Anti-Jamming Game to Combat Intelligent Jamming for Cognitive Radio Networks

Anti-Jamming Game to Combat Intelligent Jamming for Cognitive Radio Networks

Entice to Trap: Enhanced Protection against a Rate-Aware Intelligent Jammer in Cognitive Radio Networks

A multi‐agent reinforcement learning anti‐jamming method with partially overlapping channels

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