A multi objective optimization modeling in WSN for enhancing the attacking efficiency of node capture attack

Priyanka Ahlawat () and Rohit Bathla ()
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Priyanka Ahlawat: NIT
Rohit Bathla: NIT

International Journal of System Assurance Engineering and Management, 2023, vol. 14, issue 6, No 13, 2187-2207

Abstract: Abstract Node capture attacks compromise the integrity and confidentiality of networks by capturing nodes within them and obtaining cryptographic keys through wireless sensor network links. Adversarial modelling research is being conducted in order to develop efficient solutions for WSN security. Based on random key distribution scheme, an optimal graph-based technique is used to imitate node capture attack. The approach to estimating destructiveness is referred after evaluation of the relationships between keys to paths and nodes that uses graph-based modelling. The most destructiveness among all captured nodes is then secured through an optimal dominant graph (ODG), that simultaneously enhances attacking efficiency and speeds up execution. To examine the functionality of ODG with regard to the effectiveness within network, we created optimal attacking graphs, especially the optimal key graph (OKG) and optimal crossover graph (OCG). Proposed model multi objective optimization dominating algorithm (MOODA) in order to produce a path vulnerability matrix with the best possible accuracy using different objective functions that consist of multiple objectives, including dominating set of nodes, node contribution, key contribution, cost and crossover edges, to determine optimal node. The proposed matrix-based attack model by taking advantage of multiple vulnerabilities in the networks. We control a variety of processes to monitor system performance. Results show that MOODA obtain better result in increases attacking performance, decreases the total rounds, reduces accessing time, and saves cost of energy than other state of the art methods.

Keywords: Attacker efficiency; Adversary modelling; Wireless sensor network; Energy cost (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s13198-023-02048-2

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