Security threats, detection, and countermeasures for physical layer in cognitive radio networks: A survey

Salahdine, Fatima; Kaabouch, Naima

doi:10.1016/j.phycom.2020.101001

Cited by 59 publications

(24 citation statements)

References 130 publications

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“…In contrast to traditional cryptography-based authentication, physical layer authentication can fill the gap of the security mechanism in the physical layer. Furthermore, since the secure fundamental is based on physical properties, the physical layer authentication is free from key distribution and management, which is more suitable for heterogeneous networks, dense networks, low-complex IoT devices and other 5G networks [8], [14]- [17].…”

Section: A Spoofing Attack Detectionmentioning

confidence: 99%

Physical Layer Spoofing Attack Detection in MmWave Massive MIMO 5G Networks

Wang

Jiao

et al. 2021

IEEE Access

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Identity spoofing attacks pose one of the most serious threats to wireless networks, where the attacker can masquerade as legitimate users by modifying its own identity. Channel-based physicallayer security is a promising technology to counter identity spoofing attacks. Although various channelbased security technologies have been proposed, the study of channel-based spoofing attack detection in 5G networks is largely open. This paper introduces a new channel-based spoofing attack detection scheme based on channel virtual (or called beamspace) representation in millimeter wave (mmWave) massive multipleinput and multiple-output (MIMO) 5G networks. The principal components of channel virtual representation (PC-CVR) are extracted as a new channel feature. Compared with traditional channel features, the proposed features can be more sensitive to the location of transmitters and more suitable to mmWave 5G networks. Based on PC-CVR, we offer two detection strategies to achieve the spoofing attack detection tackling static and dynamic radio environments, respectively. For the static radio environment where the channel correlation is stable, Neyman-Pearson (NP) testing-based spoofing attack detection is provided depending on the 2 -norm of PC-CVR. For the dynamic radio environment where the channel correlation is changing, the problem of spoofing attack detection is transformed into a one-class classification problem. To efficiently handle this problem, an online detection framework based on a feedforward neural network with a single hidden layer is presented. Simulation results evaluate and confirm the effectiveness of the proposed detection schemes. For the static radio environment, the detection rate can be improved around 25% with the help of PC-CVR under the NP testing-based detection, and the detection accuracy can reach 99% with the machine learning-based scheme under the dynamic radio environment. INDEX TERMSPhysical layer security, Spoofing attack detection, MmWave communication, Virtual channel.

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Section: A Spoofing Attack Detectionmentioning

confidence: 99%

Physical Layer Spoofing Attack Detection in MmWave Massive MIMO 5G Networks

Wang

Jiao

et al. 2021

IEEE Access

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“…During the sensing process, SUs, under this attack, may detect attacker signals as primary user signals and then refrain from using the spectrum. A range of countermeasures against PUE attack has been investigated in the literature [109]. These countermeasures can be categorized into four types: countermeasures based on cryptography, countermeasures based on fingerprint, countermeasures based on game theory, and hybrid countermeasures that combine the three other types [109].…”

Section: Some Of the Cybersecurity Attacks In Cognitive Radiomentioning

confidence: 99%

“…A range of countermeasures against PUE attack has been investigated in the literature [109]. These countermeasures can be categorized into four types: countermeasures based on cryptography, countermeasures based on fingerprint, countermeasures based on game theory, and hybrid countermeasures that combine the three other types [109]. Spectrum sensing data falsification (SSDF) attack, also known as Byzantine attack, specially targets cooperative spectrum sensing and consists in compromising the fusion center with false spectrum sensing results to deceive decisionmaking.…”

Section: Some Of the Cybersecurity Attacks In Cognitive Radiomentioning

confidence: 99%

A Review on SDR, Spectrum Sensing, and CR-based IoT in Cognitive Radio Networks

Kassri¹,

En‐Nouaary²,

Bah³

et al. 2021

IJACSA

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The inherent scarcity of frequency spectrum, along with the fixed spectrum allocation adopted policy, has led to a dire shortage of this indispensable resource. Furthermore, with the tremendous growth of wireless applications, this problem is intensified as the unlicensed frequency spectrum becomes overcrowded and unable to meet the requirement of emerging radio devices operating at higher data rates. Additionally, the already assigned spectrum is underutilized. That has prompted researchers to look for a way to address spectrum scarcity and enable efficient use of the available spectrum. In this context, Cognitive Radio (CR) technology has been proposed as a potential means to overcome this issue by introducing opportunistic usage to less congested portions of the licensed spectrum. In addition to outlining the fundamentals of Cognitive Radio, including Dynamic Spectrum Access (DSA) paradigms and CR functions, this paper has a three-fold objective: first, providing an overview of Software Defined Radio (SDR), in which the architecture, benefits, and ongoing challenges of SDR are presented; second, giving an extensive review of spectrum sensing, covering sensing types, narrowband and wideband sensing schemes with their pros and cons, Machine Learningbased sensing, and open issues that need to be further addressed in this field; third, exploring the use of Cognitive Radio in the Internet of Things (IoT) while highlighting the crucial contribution of CR in enabling IoT. This Review is elaborated in an informative fashion to help new researchers entering the area of Cognitive Radio Networks (CRN) to easily get involved.

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“…Introduction: The eavesdropping attack has been a long-term cyber security threat in wireless and cognitive radio networks, and it is carried out by tuning an unauthorized device to a proper frequency to overhear radio-frequency (RF) signals transmitted from legitimate users [1]. Such attacks can be categorized into two types: active eavesdropping and passive eavesdropping.…”

mentioning

confidence: 99%

On non‐destructive detection of hidden passive radio‐frequency eavesdroppers

Zhang

Liang

2021

Electronics Letters

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This paper investigates the possibility of detecting the presence of hidden radio-frequency eavesdroppers embedded in modern electronic devices. The detection of such cyber security threats is quite difficult as they intercept wireless signals passively and never transmit them. The proposed detection approach relies on the fact that the impedance of active radio-frequency components in the eavesdropper circuit depends on their biased voltage, and that scattering characteristics of the eavesdropper can be affected by its internal impedance change. Using the device under test as a reflector, scattering characteristics and channel state information are measured. By comparing channel state information differences for the device under test in different power supply states, the presence of hidden eavesdroppers can be identified. Full-wave numerical simulations are performed to verify the effectiveness of the proposed method. As a non-destructive testing approach, the proposed method exhibits a robust and easy way to detect hidden eavesdroppers.

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Security threats, detection, and countermeasures for physical layer in cognitive radio networks: A survey

Cited by 59 publications

References 130 publications

Physical Layer Spoofing Attack Detection in MmWave Massive MIMO 5G Networks

Physical Layer Spoofing Attack Detection in MmWave Massive MIMO 5G Networks

A Review on SDR, Spectrum Sensing, and CR-based IoT in Cognitive Radio Networks

On non‐destructive detection of hidden passive radio‐frequency eavesdroppers

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