Classification of HHO-based Machine Learning Techniques for Clone Attack Detection in WSN
- Title
- Classification of HHO-based Machine Learning Techniques for Clone Attack Detection in WSN
- Creator
- Vatambeti R.; Damera V.K.; Karthikeyan H.; Manohar M.; Sharon Roji Priya C.; Mekala M.S.
- Description
- Thanks to recent technological advancements, low-cost sensors with dispensation and communication capabilities are now feasible. As an example, a Wireless Sensor Network (WSN) is a network in which the nodes are mobile computers that exchange data with one another over wireless connections rather than relying on a central server. These inexpensive sensor nodes are particularly vulnerable to a clone node or replication assault because of their limited processing power, memory, battery life, and absence of tamper-resistant hardware. Once an attacker compromises a sensor node, they can create many copies of it elsewhere in the network that share the same ID. This would give the attacker complete internal control of the network, allowing them to mimic the genuine nodes' behavior. This is why scientists are so intent on developing better clone assault detection procedures. This research proposes a machine learning based clone node detection (ML-CND) technique to identify clone nodes in wireless networks. The goal is to identify clones effectively enough to prevent cloning attacks from happening in the first place. Use a low-cost identity verification process to identify clones in specific locations as well as around the globe. Using the Optimized Extreme Learning Machine (OELM), with kernels of ELM ideally determined through the Horse Herd Metaheuristic Optimization Algorithm (HHO), this technique safeguards the network from node identity replicas. Using the node identity replicas, the most reliable transmission path may be selected. The procedure is meant to be used to retrieve data from a network node. The simulation result demonstrates the performance analysis of several factors, including sensitivity, specificity, recall, and detection. 2023, Modern Education and Computer Science Press. All rights reserved.
- Source
- International Journal of Computer Network and Information Security, Vol-15, No. 6, pp. 1-15.
- Date
- 2023-01-01
- Publisher
- Modern Education and Computer Science Press
- Subject
- Battery Resource; Clone Attack; Horse Herd Metaheuristic Optimization Algorithm; Optimized Extreme Learning Machine; Wireless Sensor Network
- Coverage
- Vatambeti R., School of Computer Science and Engineering, VIT-AP University, Vijayawada, 522237, India; Damera V.K., Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad, 500075, India; Karthikeyan H., Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, 560074, India; Manohar M., Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, 560074, India; Sharon Roji Priya C., Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, 560074, India; Mekala M.S., School of Communication Engineering, Yeungnam University, Gyeongsan, 38541, South Korea
- Rights
- All Open Access; Gold Open Access
- Relation
- ISSN: 20749090
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Vatambeti R.; Damera V.K.; Karthikeyan H.; Manohar M.; Sharon Roji Priya C.; Mekala M.S., “Classification of HHO-based Machine Learning Techniques for Clone Attack Detection in WSN,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 27, 2025, https://archives.christuniversity.in/items/show/13948.