Detecting Byzantine attack in cognitive radio networks using machine learning
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Detecting Byzantine attack in cognitive radio networks using machine learning Rupam Sarmah1 • Amar Taggu1 • Ningrinla Marchang1
Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract One primary function in a cognitive radio network (CRN) is spectrum sensing. In an infrastructure-based CRN, instead of individual nodes independently sensing the presence of the incumbent signal and taking decisions thereon, a fusion center (FC) aggregates the sensing reports from the individual nodes and makes the final decision. Such collaborative spectrum sensing (CSS) is known to result in better sensing accuracy. On the other hand, CSS is vulnerable to Spectrum Sensing Data Falsification (SSDF) attack (a.k.a. Byzantine attack) wherein a node maliciously falsifies the sensing report prior to sending it to the FC, with the intention of disrupting the spectrum sensing process. This paper investigates the use of machine learning techniques, viz., SVM, Neural Network, Naive Bayes and Ensemble classifiers for detection of SSDF attacks in a CRN where the sensing reports are binary (i.e., either 1 or 0). The learning techniques are studied under two experimental scenarios: (a) when the training and test data are drawn from the same data-set, and (b) when separate datasets are used for training and testing. Under the first scenario, of all the techniques, NN and Ensemble are the most robust showing consistently very good performance across varying presence of attackers in the system. Moreover performance comparison with an existing non-machine learning technique shows that the learning techniques are generally more robust than the existing algorithm under high presence of attackers. Under the second scenario, in a limited environment, Ensemble is the most robust method showing good overall performance. Keywords Vulnerability detection Spectrum sensing Machine learning Cognitive radio network Frequency property Data science
1 Introduction Cognitive radio is considered to be one technology among many that under-girds the 5G revolution [1]. There has been unprecedented growth in the development of wireless technologies in the past decades that exploit the unlicensed Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11276-020-02398-w) contains supplementary material, which is available to authorized users. & Ningrinla Marchang [email protected]; [email protected] Rupam Sarmah [email protected] Amar Taggu [email protected]; [email protected] 1
North Eastern Regional Institute of Science and Technology, Nirjuli, Itanagar, Arunachal Pradesh 791109, India
portion of the radio spectrum leading to ‘radio traffic jam’. CRNs allow the opportunistic usage of the licensed spectrum bands by permitting unlicensed users (a.k.a. secondary users (SUs)) to use them when they are unoccupied by the users (a.k.a. primary users (PUs)) to which they are licensed. Hence, cognitive radio technology is considered a significant solution fo
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