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Article type: Research Article
Authors: Ramachandran, Dhanagopala | Venkatesh, J.b; * | Jothilakshmi, R.c | Gugapriya, G.d
Affiliations: [a] Centre for System Design, Deptartment Of ECE, Chennai Institute of Technology, Chennai, Tamil Nadu, India | [b] Department of Computer Science and Engineering, Chennai Institute of Technology, Kundrathur, Chennai, Tamil Nadu, India | [c] Department of Information Technology, R.M.D. Engineering College, Chennai, Tamil Nadu, India | [d] School of Electronics Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India
Correspondence: [*] Corresponding author. Dr.J. Venkatesh, Professor, Department of Computer Science and Engineering, Chennai Institute of Technology, Kundrathur, 600069, Chennai, Tamil Nadu, India. E-mail: [email protected].
Abstract: Since there is no central controller, preserving the security and energy efficiency of wireless sensor networks (WSN) is challenging. They also have a flexible configuration. A network of this type is vulnerable to several attacks. The main goal of this paper is to focus on a well-known attack known as the sinkhole attack. Sensors are installed and positioned equally in a WSN to communicate sensed data to a centralized station regularly. So, the sinkhole attack is a big danger to the WSN network layer, and it is still a difficult issue on sensor networks, where even the malicious node collects packets from other regular sensor nodes and dumps them. To maintain the integrity and authentication of data during its travel in wireless sensor networks overcoming sinkhole attacks we propose a novel approach. In our approach besides overcoming sinkhole attack using a threshold-based method, authentication, and data integrity is maintained using a watermarking-based technique.
Keywords: Ad-hoc On-demand Distance Vector (AODV), Binary Logistic Regression (BLR), Intrusion Detection System (IDS), Low Energy Adaptive Clustering Hierarchy (LEACH), Machine Learning, Wireless Sensor Network (WSN), Network Simulator (NS), Statistical Analysis (SA), Support Vector Machine (SVM)
DOI: 10.3233/JIFS-224463
Journal: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 7005-7023, 2023
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