A survey on security issues in cognitive radio based cooperative sensing

Shrivastava, Shivanshu; Rajesh, A.; Bora, Prabin Kumar; Chen, Bin; Dai, Mingjun; Lin, Xiaohui; Hui, Wang

doi:10.1049/cmu2.12131

Cited by 10 publications

(10 citation statements)

References 123 publications

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“…19 Shrivastava et al performed a state-of-the-art comprehensive survey of the researches on defending spectrum sensing data falsification and primary user emulation attack. 20 Fragkiadakis et al presented an overview of the security threats and challenges that cognitive radios and cognitive radio networks face, along with the current state-of-the-art to detect the corresponding attacks. 21 Sun et al investigated the physical layer security in cognitive radio networks, which consists of a source node, a destination node, an eavesdropper, and multiple relays.…”

Section: Related Workmentioning

confidence: 99%

“…Manohar et al incorporated an artificial intelligence approach called reinforcement learning into a trust model to countermeasure the random and intelligent attacks 19 . Shrivastava et al performed a state‐of‐the‐art comprehensive survey of the researches on defending spectrum sensing data falsification and primary user emulation attack 20 . Fragkiadakis et al presented an overview of the security threats and challenges that cognitive radios and cognitive radio networks face, along with the current state‐of‐the‐art to detect the corresponding attacks 21 …”

Section: Related Workmentioning

confidence: 99%

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Employing machine learning based malicious signal detection for cognitive radio networks

TÜRKYILMAZ

Şentürk

Bayrakdar

2022

Concurrency and Computation

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In cognitive radio networks, the empty spectrum that is also named as spectrum hole is detected with the help of spectrum sensing techniques. Energy detection is the most utilized spectrum sensing technique owing to its low complexity. In the energy detection technique, a spectrum hole is detected with a predefined threshold. In this article, machine learning based malicious signal detection is employed for cognitive radio networks. The design of cognitive radio users and network environment is simulated with Riverbed simulation software. The received signal is controlled whether it is a malicious signal or just a secure sensing signal. The fuzzy logic based system is utilized for the security categorization of spectrum sensing signals as malicious, suspicious, and secure sensing signals. Fuzzy logic parameters are taken from the machine learning features that are chosen as the most effective 3 features among all 49 features. The security of primary users is enhanced when compared to other schemes in the literature. The results of the proposed machine learning based malicious signal detection system are validated with the results acquired from the fuzzy logic based approach.The random forest method gives the best results among all machine learning methods for the detection of signals.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Employing machine learning based malicious signal detection for cognitive radio networks

TÜRKYILMAZ

Şentürk

Bayrakdar

2022

Concurrency and Computation

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“…Then, cooperative spectrum sensing (CSS) is proposed to achieve cooperative gain by exploiting spatial diversity and enhance the spectrum sensing performance. [4][5][6] But this cooperative paradigm also opens a window for malicious users (MUs) to launch Byzantine attack, who may manipulate their sensing results and impair the cooperation process at the fusion center (FC), 7,8 therefore undermining the premise of the CR network (CRN). Hence, how to efficiently and effectively defend against Byzantine attack is necessary for CSS to identify available spectrum resources.…”

Section: Introductionmentioning

confidence: 99%

Sequential spectrum sensing against random Byzantine attack in cognitive radio networks

Wu,

He,

Gan

et al. 2023

Trans Emerging Tel Tech

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Cognitive radio (CR) technology allows secondary users (SUs) to opportunistically access the licensed spectrum being authorized by the primary users (PUs) and then improves the spectrum utilization. Cooperative spectrum sensing (CSS) is an important component of cognitive radio (CR) technology to detect the PU signal via spatial diversity. However, due to the open facet of CR networks (CRNs), the CSS process may suffer from Byzantine attack where malicious users (MUs) falsify the status information about the PU signal and then cause harmful interference to the PU's communication or selfishly occupy spectrum resources. Otherwise, the cooperative paradigm also results in that multiple SUs need more time to detect the PU signal. The performance and efficiency of CSS will be further degraded due to these two reasons. For this aim, we propose sequential spectrum sensing (SSS) scheme to mitigate the negative impact of Byzantine attack and improve the cooperative efficiency. To this end, encouraged by in‐depth analysis of the blind problem, we formulate a random Byzantine attack model to follow the malicious behaviors from the perspective of MUs. In order to solve the blind problem in the process of the sensing sample fusion, a delivery evaluation mechanism is developed to lay solid foundation for SSS. On this basis, we further evaluate the reputation value (RV) within a sliding window and in order to improve the cooperative efficiency in the reporting stage, we dynamically select the sensing results of the more reliable SUs as the global decision to make a tradeoff between cooperative performance and cooperative efficiency. At last, extensive simulation results show that regardless of the malicious ratio, our proposed SSS only requires 6 samples to provide with 100% detection accuracy in the presence of always attack while also the remarkable performance in the presence of random attack when the malicious ratio exceeds 50%.

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“…In a cognitive radio network (CRN) consisting of the primary user (PU), and the secondary user (SU), 2 two kinds of users have different priorities to spectrum access. By means of the spectrum sensing technology, 3 when the SU has detected that the targeted channel is underutilized by the PU and then use spectrum in an opportunistic manner 4‐8 . If the SU has detected that the PU is utilizing the targeted channel, then the SU must immediately free up the channel for the PU without causing interference to primary networks.…”

Section: Introductionmentioning

confidence: 99%

“…sensing technology, 3 when the SU has detected that the targeted channel is underutilized by the PU and then use spectrum in an opportunistic manner. [4][5][6][7][8] If the SU has detected that the PU is utilizing the targeted channel, then the SU must immediately free up the channel for the PU without causing interference to primary networks.…”

mentioning

confidence: 99%

Robust and efficient cooperative spectrum sensing against probabilistic hard Byzantine attack

Chen

Gan

et al. 2021

Trans Emerging Tel Tech

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Cognitive radio (CR) is regarded as a powerful technology to solve the problem of spectrum shortage and underutilization. As a key function of CR technology, cooperative spectrum sensing (CSS) allows secondary users (SUs) to detect the primary user (PU)'s signal so that they identify and opportunistically access the available spectrum. However, the openness of CSS paradigm makes cognitive radio networks (CRNs) suffer from Byzantine attack, thereby undermining the premise of CR framework. To this aim, we formulate a probabilistic hard Byzantine attack model, in which malicious users (MUs) can conduct various attack strategies, and make an in‐depth investigation on the blind scenario. On the one hand, in order to ensure the robustness of CSS, a method to evaluate the reliability of the secondary user (SU)'s sensing result based on the channel status detection is proposed and an innovative weight coefficient is considered to selectively utilize the sensing information from MUs. On the other hand, we design a sequential fusion method based on reputation value (RV) and differential mechanism, with the aim of improving the efficiency of CSS. According to above methods and mechanism, the weighted differential sequential symbol (WDS2) algorithm is designed, which integrates the weight evaluation into sequential method to make the global decision for CSS. Finally, compared to the existing various data fusion algorithms, simulation results show that the proposed WDS2 not only defends against various Byzantine attacks to secure the robustness of CSS, but also requires less samples in support of an accurate global decision to improve the efficiency of CSS.

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A survey on security issues in cognitive radio based cooperative sensing

Cited by 10 publications

References 123 publications

Employing machine learning based malicious signal detection for cognitive radio networks

Employing machine learning based malicious signal detection for cognitive radio networks

Sequential spectrum sensing against random Byzantine attack in cognitive radio networks

Robust and efficient cooperative spectrum sensing against probabilistic hard Byzantine attack

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