Mitigation of Jamming Attacks via Deception

Nan, Satyaki; Brahma, Swastik; Kamhoua, Charles A.; Leslie, Nandi

doi:10.1109/pimrc48278.2020.9217140

Cited by 13 publications

(6 citation statements)

References 12 publications

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“…More recently, Nan et al [34] introduced a ground-breaking Stackelberg deception game based on power allocation. They considered two pairs of a transmitter-receiver.…”

Section: Deception Based Defence Strategiesmentioning

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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“…More recently, Nan et al [34] introduced a ground-breaking Stackelberg deception game based on power allocation. They considered two pairs of a transmitter-receiver.…”

Section: Deception Based Defence Strategiesmentioning

confidence: 99%

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

et al. 2022

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“…The work in [28] proposes an anti-jamming algorithm in which "decoy" users are used to trap the jammer. Similar to [28], the authors in [29] propose to foil the jammer by dedicating a secondary user that transmits fake signals to attract a portion of the jamming power. The authors in [30] propose an anti-jamming method where a transmitter forms a decoy beam in another frequency channel than the main communication channel to distract the jammer from the main communication beam.…”

Section: A Related Workmentioning

confidence: 99%

“…As a result, the problem of finding the optimal channel allocation and user selection as a decoy are not considered. Moreover, since the works in [28] and [29] devote at least one user to secure other users' communication, they are not practical for single user scenarios. Furthermore, similar to the works in [12], [15], [16], [20], and [26], the proposed methods in [30] and [31] are restricted to specific system models since [31] employs an RF tag to back scatter information, which is not available in all the networks, and both works assume that the legitimate nodes are equipped with multi-array antennas.…”

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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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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“…Recently, a new deception mechanism has been developed to trap the jammer into attacking a decoy channel. The authors in [34] proposed to transmit fake information over a second transmitter‐receiver pair and deceive the jammer into investing some of its power budget for jamming the decoy channel. In [35], the authors extended the single‐user situation in [34] to the multi‐user scenario and further considered the selection of decoy channel.…”

Section: Introductionmentioning

confidence: 99%

A back‐to‐back coordination‐based learning scheme for deceiving reactive jammers in distributed networks

Du,

Zhang,

Qian

et al. 2024

IET Communications

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Reactive jammers select jamming strategies according to the users’ responses; thus, conventional anti‐jamming methods such as frequency hopping are inadequate to defeat the jamming attack. In this article, the authors propose a novel uncoupled deception scheme to trap the reactive jammer into attacking a decoy channel in distributed networks. Specifically, the authors design a multi‐functional network utility for every user to mislead the jammer with a minimum energy consumption while achieving the highest network throughput. Based on the network utility, the anti‐jamming problem is formulated as an exact potential game such that the existence of Nash equilibrium can be guaranteed theoretically. The authors further propose a back‐to‐back coordination‐based learning algorithm to reach the optimal channel selection and power adaption in a non‐cooperative way. To alleviate the lack of mutual information exchange, the back‐to‐back coordination mechanism derives all users to deceive the jammer by inferring others’ strategies based on a shared belief. Simulation results show that the proposed algorithm yields higher network throughput and efficiency‐cost ratio compared to the state‐of‐the‐art cooperative schemes.

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Mitigation of Jamming Attacks via Deception

Cited by 13 publications

References 12 publications

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

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

Reinforcement Learning for Deceiving Reactive Jammers in Wireless Networks

A back‐to‐back coordination‐based learning scheme for deceiving reactive jammers in distributed networks

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