Machine Learning Based Detection of Gray-Hole Attack in Mobile Ad-Hoc Networks
Publication Date
1-1-2020
Document Type
Conference Proceeding
Publication Title
3rd International Conference on Wireless, Intelligent and Distributed Environment for Communication: WIDECOM 2020
Editor
Isaac Woungang, Sanjay Kumar Dhurandher
Volume
51
DOI
10.1007/978-3-030-44372-6_13
First Page
149
Last Page
158
Abstract
Ad-hoc network consists of a cluster of mobile nodes. Data transfer among these nodes is achieved by effective communication between them. The absence of a base system makes substantial security an issue. The ad-hoc networks communication is based on a cooperative environment, which is prone to many network-based attacks. One such attack is Distributed Denial of Service (DDoS) attack. Therefore there is a need to develop an Intrusion Detection System (IDS) for malicious activity detection. In this paper, an Intrusion Detection System is proposed to detect one type of DDoS attack known as Gray-hole attack by using machine learning approach to predict malicious activities. The proposed model utilizes Support Vector Machine (SVM) to classify the malicious and non-malicious activity of the nodes. Experiment results show that the proposed model achieves 80% accuracy.
Keywords
Denial of Service Attack, Gray Hole Intrusion Detection System, Machine learning based Intrusion Detection System, Malicious node, Mobile Ad-Hoc Network (MANET), Routing Attack, Smart Gray hole attack
Department
Electrical Engineering
Recommended Citation
Sunil Poyyagadde Rao, Deepak Devaru Joshi, and Juzi Zhao. "Machine Learning Based Detection of Gray-Hole Attack in Mobile Ad-Hoc Networks" 3rd International Conference on Wireless, Intelligent and Distributed Environment for Communication: WIDECOM 2020 (2020): 149-158. https://doi.org/10.1007/978-3-030-44372-6_13
