The detection of the spectrum sensing data falsification attack in cognitive radio ad hoc networks

Ngomane, Issah; Velempini, Mthulisi; Dlamini, Sabelo

doi:10.1109/ictas.2018.8368742

Cited by 12 publications

(6 citation statements)

References 14 publications

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“…In [24] and with the aid of the Thompson sampling technique, an online primary user emulation attack was investigated. In [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], novel algorithms were proposed by considering the energy efficiency, spectrum maintenance, bandwidth guaranteeing, robustness, complexity, deep-learning hardware/software, probabilisticity, secure fusion strategies, specific channel models e.g. Nakagami-m fading etc.…”

Section: A Related Workmentioning

confidence: 99%

Adversarial Robustness in Cognitive Radio Networks

Zamanipour¹

2022

Preprint

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When an adversary gets access to the data sample in the adversarial robustness models and can make data-dependent changes, how has the decision maker consequently, relying deeply upon the adversarially-modified data, to make statistical inference? How can the resilience and elasticity of the network be literally justified from a game theoretical viewpoint − if there exists a tool to measure the aforementioned elasticity? The principle of byzantine resilience distributed hypothesis testing (BRDHT) is considered in this paper for cognitive radio networks (CRNs) − withoutloss-of-generality, something that can be extended to any type of homogeneous or heterogeneous networks. We use the temporal rate of the 𝛼−leakage as the appropriate tool which we measure the aforementioned resilience through. We take into account the main problem from an information theoretic point of view via an exploration over the adversarial robustness of distributed hypothesis testing rules. We chiefly examine if one can write F = 𝑚𝑎 for the main problem, consequently, we define a nested bilevel − even 3-level including a hidden control-law − mean-fieldgame (MFG) realisation solving the control dynamics as well. Further discussions are also provided e.g. the synchronisation. Our novel online algorithm − which is named OBRDHT − and solution are both unique and generic over which an evaluation is finally performed by simulations.

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Section: A Related Workmentioning

confidence: 99%

Adversarial Robustness in Cognitive Radio Networks

Zamanipour¹

2022

Preprint

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“…Cyber threats including such jamming [2] and GPS spoofing makes 5G, like all other networks, vulnerable [3,4]. Cognitive radio can render these risk taking to new attacks [5][6][7][8], such as main customer emulation [5] and data falsification through spectrum sensing [6]. As a result, it's crucial to understand the cyber security effects of 5Mobile communications [8,9].…”

Section: Introductionmentioning

confidence: 99%

Detection of jamming and interference attacks in wireless communication network using deep learning technique

Manikanthan¹,

Padmapriya²

2021

Proceedings of the First International Conference on Computing, Communication and Control System, I3CAC 2021, 7-8 June 2021, Bh

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The Jamming and interference attacks aim to disable a wireless network, inducing a denial of service. Despite the resilience offered 5G is prone to these regarding the impact to the use of millimetre wave bands. In the last decade, several jamming detection techniques have been developed, including fuzzy logic, game theory, channel surfing, and some others statistical modeling. The plurality of these strategies are inadequate at detecting smart jammers. As a response, efficient and quick jamming and interference high-accuracy detection systems are all still in great demand. The usefulness of many deep learning models in detecting jamming and interference signals is analyzed in this paper. The types of signal features that could be used to diagnose jamming and interference signals are investigated, and a large dataset was created using these parameters. Deep learning algorithms are being kitted, tested, and sorely tested using this dataset. Logistic regression and naïve bayes are representations of these algorithms. The probability of detection, probability of false alarm and accuracy are being used to verify and validate the performance of these algorithms. The simulation results show that a logistic regression algorithm based on jamming detection and interference can detect jammers with perfect seating, a high possibility of detection, and a minimal probability of false alarm.

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“…Like the existing networks, 5G is vulnerable to the cyber security attacks, including jamming [2] and GPS spoofing [3,4]. It will be enabled by cognitive radio, making these networks open to new attacks, including primary user emulation attacks [5] and spectrum sensing data falsification [6]. Thus, it is important to explore the cybersecurity implications of 5G systems [7,8].…”

Section: Introductionmentioning

confidence: 99%

A Novel Jamming Attacks Detection Approach Based on Machine Learning for Wireless Communication

Arjoune

Salahdine

Islam

et al. 2020

2020 International Conference on Information Networking (ICOIN)

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Jamming attacks target a wireless network creating an unwanted denial of service. 5G is vulnerable to these attacks despite its resilience prompted by the use of millimeter wave bands. Over the last decade, several types of jamming detection techniques have been proposed, including fuzzy logic, game theory, channel surfing, and time series. Most of these techniques are inefficient in detecting smart jammers. Thus, there is a great need for efficient and fast jamming detection techniques with high accuracy. In this paper, we compare the efficiency of several machine learning models in detecting jamming signals. We investigated the types of signal features that identify jamming signals, and generated a large dataset using these parameters. Using this dataset, the machine learning algorithms were trained, evaluated, and tested. These algorithms are random forest, support vector machine, and neural network. The performance of these algorithms was evaluated and compared using the probability of detection, probability of false alarm, probability of miss detection, and accuracy. The simulation results show that jamming detection based random forest algorithm can detect jammers with a high accuracy, high detection probability and low probability of false alarm.

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The detection of the spectrum sensing data falsification attack in cognitive radio ad hoc networks

Cited by 12 publications

References 14 publications

Adversarial Robustness in Cognitive Radio Networks

Adversarial Robustness in Cognitive Radio Networks

Detection of jamming and interference attacks in wireless communication network using deep learning technique

A Novel Jamming Attacks Detection Approach Based on Machine Learning for Wireless Communication

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