Combating PUE Attack in Cognitive Radio Networks using RSSI Based EKF and UKF
Kattaswamy Mergu*, Ph.D Scholor, Electronics and Communication, Sri Satya Sai University of Technology and Medical Sciences, Sehore, Madhya Pradesh India.
Manuscript received on November 21, 2019. | Revised Manuscript received on December 30, 2019. | Manuscript published on December 30, 2019. | PP: 5108-5114 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B3886129219/2019©BEIESP | DOI: 10.35940/ijeat.B3886.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The problem of spectrum scarcity in wireless communication can be reduced by Cognitive Radio (CR) technology in which the spectrum holes or unused spectrum can be allocated to the secondary or unlicensed users. But the major problem in CR is providing security. Various security issues are present at different layer. One of the major wide spread security issue in the cognitive radio is the Primary user emulsion attack in which the malicious secondary user emulated as primary user to get spectrum resources for a long time. One of way to avoided PUEA is, by obtaining the location of malicious user. The conventional location detection techniques such as time of arrival, time difference of arrival and direction of arrival, will give better performance when the user is stationary. Even though Received Signal Strength Indicator along RF fingerprint technique gives the better location of mobile user but it requires the more hardware. Hence the cost is high. In this paper, the author proposed an algorithm to locate the attacker using EKF and UKF with RSSI. In this algorithm, the initial position of user can be obtained by Received Signal Strength Indicator. This initial position integrated to EKF and UKF to track the location of primary user, which is a mobile user so that PUE Attack can be identified and avoided. The author also compares the performance of Extended Kalman Filter with Unscented Kalman Filter by Matlab software.
Keywords: Cognitive Radio, Extended Kalman Filter, Primary User Emulsion Attack, Received Signal Strength Indicator, Unscented Kalman Filter