Mitigating Gray Hole attack in Mobile AD HOC Network using Artificial Intelligence Mechanism
Puneet Kamal1, Rajeev Sharma2, Abhishek Gupta3, Gaurav Kumar4
1Puneet kamal, Department of Computer Science and Engineering, Chandigarh Engineering College, Landran (Mohali), India.
2Rajeev Sharma, Department of Computer Science and Engineering, Chandigarh Engineering College, Landran (Mohali), India.
3Abhishek Gupta, Department of Computer Science and Engineering, Chandigarh Engineering College, Landran (Mohali),India.
4Gaurav Kumar, Department of Computer Science and Engineering, Chandigarh Engineering College, Landran (Mohali), India.
Manuscript received on 05 August 2019 | Revised Manuscript received on 12 August 2019 | Manuscript Published on 26 August 2019 | PP: 640-645 | Volume-8 Issue-9S August 2019 | Retrieval Number: I11020789S19/19©BEIESP | DOI: 10.35940/ijitee.I1102.0789S19
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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: A mobile ad hoc network (MANET) is a combination of multiple mobile nodes, which are interconnected by radio link. In MANET, sensor nodes are free to move, and each node can act as a host or router. Routing is one of the most challenging tasks because nodes move frequently. Therefore, in MANET, the routing protocol plays an important role in selecting the best route to efficiently transmit data from the source node to the destination node. In this paper, the best path with efficient Ad Hoc on Demand Distance Vector (AODV) routing protocol is chosen as the routing mechanism. The properties of each node are categorized using firefly algorithm. The Artificial Neural Network (ANN) is trained as per these properties and hence in case if the gray hole node is detected within the route, it is identified and the route between the source and the destination is changed. At last, to show how effectively the proposed AODV with Firefly and ANN works is computed in terms of performance parameters. The throughput and PDR is increased by 4.13 % and 3.15 % compared to the network which is affected by gray hole attack. The energy up to 44.02 % has been saved.
Keywords: Mobile ad hoc Network, Gray hole Attack, Cuckoo Search, Support Vector Machine, Ad Hoc On-Demand Distance Vector.
Scope of the Article: Mobile Networks & Wireless LAN