Cognitive Radio Networks for Detecting Malicious Nodes
Dinokumar Kongkham1, M Sundararajan2
1Dinokumar Kongkham, Research Scholar/ECE, Bharath Institute of Higher Education and Research, Chennai, (Tamil Nadu), India.
2M Sundararajan, Professor/ECE, Bharath Institute of Higher Education and Research, Chennai, (Tamil Nadu), India.
Manuscript received on 23 March 2019 | Revised Manuscript received on 30 March 2019 | Manuscript published on 30 March 2019 | PP: 646-653 | Volume-7 Issue-6, March 2019 | Retrieval Number: F2748037619/19©BEIESP
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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: Nowadays cognitive radio technologies are emerging technology for effective communication with high throughput. For analyzing effective wireless communication we surveyed several journals and discussed in the section II. SDR also implemented for easy implementation. Spectrum utilization is a major role for a better communication. Each PU has a unique license but sometimes, primary user not using their spectrum. In cognitive radio allow secondary user for using unused licensed spectrum of PU but many secondary users try to access PU at a same time and its leads to primary emulation attack. So first we studied how to avoid primary emulation attack. PEA reduced by using request priority if one SU only access the PU at a time other SU were in the wait state. Analyzing SU request all times. Attackers send many requests to the resources node then if request reach beyond threshold limits means immediately PU block that attacker node. Each node configured with standard bandwidth. Each nodes were estimated with several parameters like signal strength, battery level, distance etc.. in proposed method first formed a own ad-hoc network structure. Protecting PU from an attackers using threshold analysis method. We survived several authors’ works and demonstrated our results in section III using ns2 simulator. We improved 6-7% percentage throughput and minimized interference also.
Keywords: Cognitive Radio Network (CRN), Interference, Primary User (PU); Secondary user (SU); Primary Emulation Attack (PEA).
Scope of the Article: Cognitive Radio Networks