When Attackers Meet AI: Learning-Empowered Attacks in Cooperative Spectrum Sensing

Luo, Zhaohua; Zhao, Shangqing; Lu, Zhuo; Xu, Jie; Sagduyu, Yalin E.

doi:10.1109/tmc.2020.3030061

Cited by 50 publications

(25 citation statements)

References 57 publications

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“…Another works are also based on cooperative sensing spectrum, though, the decision is taken by the fusion center in [189]. Indeed the nodes report their sensing results to a fusion center that makes a centralized decision on the availability of channels.…”

Section: • Adversarial Attacks On Cognitive Radiosmentioning

confidence: 99%

How Machine Learning Changes the Nature of Cyberattacks on IoT Networks: A Survey

Bout

Loscrì

Gallais

2022

IEEE Commun. Surv. Tutorials

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Section: • Adversarial Attacks On Cognitive Radiosmentioning

confidence: 99%

How Machine Learning Changes the Nature of Cyberattacks on IoT Networks: A Survey

Bout

Loscrì

Gallais

2022

IEEE Commun. Surv. Tutorials

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“…Poisoning attacks, also called causative attacks, provide incorrect information such as training data to the ML to cause the ML model to perform poorly. The poisoning attacks in the context of SS networks have been investigated in [162], which can be achieved by fooling the classifiers with spectrum data falsifications during the CSS phase, similar to SSDF attacks.…”

Section: Attacks For ML Modelmentioning

confidence: 99%

When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges

Wang

Sun

et al. 2022

IEEE Open J. Commun. Soc.

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The exponential growth of internet connected systems has generated numerous challenges, such as spectrum shortage issues, which require efficient spectrum sharing (SS) solutions. Complicated and dynamic SS systems can be exposed to different potential security and privacy issues, requiring protection mechanisms to be adaptive, reliable, and scalable. Machine learning (ML) based methods have frequently been proposed to address those issues. In this article, we provide a comprehensive survey of the recent development of ML based SS methods, the most critical security issues, and corresponding defense mechanisms. In particular, we elaborate the state-of-the-art methodologies for improving the performance of SS communication systems for various vital aspects, including ML based cognitive radio networks (CRNs), ML based database assisted SS networks, ML based LTE-U networks, ML based ambient backscatter networks, and other ML based SS solutions. We also present security issues from the physical layer and corresponding defending strategies based on ML algorithms, including Primary User Emulation (PUE) attacks, Spectrum Sensing Data Falsification (SSDF) attacks, jamming attacks, eavesdropping attacks, and privacy issues. Finally, extensive discussions on open challenges for ML based SS are also given. This comprehensive review is intended to provide the foundation for and facilitate future studies on exploring the potential of emerging ML for coping with increasingly complex SS and their security problems.

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“…When compared to data transmission jamming, this attack uses less energy and is harder to detect. The intrusion is effective and significantly decreases the trans mitter's throughput [25][26][27][28][29].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Researchers in [32][33][34] investigated the attacks 802.11 networks. Similarly, authors in [35][36][37][38] and [7][8][9][10][11], [24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39] have investigated the attacks in sensor networks, multi-hop networks, and other network models respectively. Sadeghi and Larsson [40] have described how blackbox adversarial attacks are carried out and their impact on the transmission system's block error rate.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of performance vulnerability of MAC scheduling algorithms due to SYN flood attack in 5G NR mmWave

Saikia¹,

Majumder²

2021

IJATEE

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Fifth-generation (5G) new radio (NR) millimetre wave (mmWave) is a kind of 5G network that operates in the 24GHz to 100GHz frequency range. It offers several opportunities as well as numerous challenges. One of the most prominent challenges that a 5G network faces is intrusion. Intrusion is possible because of existing vulnerabilities in the 5G NR mmWave network architecture. We exploited one such vulnerabil ity to create synchronise (SYN) flood intrusion into the network. The SYN flood intrusion is a denial of service (DoS) intrusion. The intruder who is involved in the SYN flood intrusion depletes the network's available resources. As a result, it denies genuine UEs/nodes access to the network's services and resources. Since this attack produces many open connections with the server, it slows down media access control (MAC) schedulers' ability to assign available channels to the user equipment. In this article, we proposed a method to exploit existing vulnerabilities of the 5G NR mmWave network to carry out SYN flood attacks. Further, we investigated the effect of the attack on the performance of the MAC schedulers, such as proportionate fair and round robin MAC schedulers. With the addition of SYN flood attack UEs/nodes, we observed that the throughput for proportional fair and round robin MAC schedulers drops dramatically.In the event of an attack, throughput drops by 2.34 % to 37.7%. However, in the event of an SYN flood attack, network delay and jitter increase. The performance of the network suffers as a result of that.

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When Attackers Meet AI: Learning-Empowered Attacks in Cooperative Spectrum Sensing

Cited by 50 publications

References 57 publications

How Machine Learning Changes the Nature of Cyberattacks on IoT Networks: A Survey

How Machine Learning Changes the Nature of Cyberattacks on IoT Networks: A Survey

When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges

Analysis of performance vulnerability of MAC scheduling algorithms due to SYN flood attack in 5G NR mmWave

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