AORA-A Novel Optimized Intrusion Detection System for Identification of the Black Hole Attacks in Wireless Sensor Networks
S. Sridevi1, R. Anandan2
1S. Sridevi, Assistant Professor, Vels University, Chennai (Tamil Nadu), India.
2Dr. R. Anandan, Professor, Vels University, Chennai (Tamil Nadu), India.
Manuscript received on 05 February 2019 | Revised Manuscript received on 18 February 2019 | Manuscript Published on 04 March 2019 | PP: 144-151 | Volume-7 Issue-5S2 January 2019 | Retrieval Number: ES2020017519/19©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: The application of Wireless Sensor Networks finds its function in all the application areas like Health care, Automation, Agriculture and others. Along with the IoT (Internet of Things), WSN plays a very important role in data collection which is used for the monitoring and control. Even though WSN plays a more noteworthy role in the collection, monitoring and control, WSN suffers a serious setback in the form of different attacks which manipulates the data or even the nodes. To overcome this setback, IDS (Intrusion detection System) has been placed to guarantee the stability and security of the Wireless Sensor Networks. Several IDS has been implemented, but challenges increases day by day.As first step towards intelligent IDS, this paper proposes the new algorithm AORA (Advanced Optimizer for Reliable Allocation) which mechanism on the powerful BAT optimizer integrated with Cognitive learning machines (CLM). The proposed algorithm has been tested with the two scenarios such as AODV and LEACH environment and accuracy of detection is determined for several test cases. The proposed algorithm has been compared by implementing the other optimization algorithms method such as different PSO and GA in which the proposed optimizer outperforms and other algorithms in terms of accuracy of detection (AID), and throughput.
Keywords: AORA, BAT, PSO, GA Cognitive Learning Machines (CLM), AODV, LEACH.
Scope of the Article: Wireless ad hoc & Sensor Networks