Spam Detection and Recovery Model for WSN
Ashish Kumar Srivastava1, Aditya Goel2, Jyoti Srivastava3
1Ashish Kumar Srivastava, B. E, in Department of Electrical and Electronics Engineering From Bharathiyar University Coimbatore Tamil Nadu, and M. Tech in Information Technology From Tezpur Central University Assam.
2Aditya Goel, Associate Professor in the Department of Electronics & Communication Engineering at Maulana Azad National Institute of Technology (Deemed University), Bhopal, India.
3Jyoti Srivastava, Assistant Professor in Department of Madanapalle Institute of Technology and Science, Madanapalle, Andhra Pradesh. Jyoti has Completed her M. Tech and PhD From IIIT Allahabad
Manuscript received on 14 March 2019 | Revised Manuscript received on 19 March 2019 | Manuscript published on 30 July 2019 | PP: 2894-2899 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2289078219/19©BEIESP | DOI: 10.35940/ijrteB2289.078219
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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: Wireless sensor network (WSN) is susceptible tovarious kind of security attacks; one among them which is more prevalent is DoS(Denial of Service) attack. Spam message consumes most of the node energy and cause energy constraints node to drain with energy. So the prevention of such kind of attack is required in WSN. To solve this issue we are using a systematic approach of identifying spam message which may leads to the DoS attack. We are using a set of mobile agents which perform specifically assigned task to control overflow of excess Spam messages so as to stop DoS attack. The proposed spam prevention model for the MA (Mobile Agent) is experimented on WSN. The proposed model Restrict the unnecessary MAs roaming and cloning are minimized and thus the energy can be saved. To calculate the threshold value we have used two techniques; one is based on the number of itinerary the clone threshold is set and the other is based on the simple moving average method so as to calculate the threshold value for the number of remote MAs. Based on the previous threshold values the new threshold value is calculated. The analysis shows that as the node is increasing from 1 to 10 how drastically Anti MA’s clone generated in when DADR model is not used and how the number reduces when DADR model is used. Subsequently energy is saved by applying DADR model. Through this proposed model DoS attack detection and Recovery Model (DADR), we can avoid the spam attack in the MA based wireless sensor environment and save energy of resource constraints WSN and hence increase its life.
Key Words: Mobile Agent, Wireless Sensor Network, Denial of Service Attack, Spam.
Scope of the Article: Wireless ad hoc & Sensor Networks