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<p>Wireless sensor networks (WSN) have seen immense use in everyday life, like health, battle-field administration, and disaster administration. Nodes inside WSN are more vulnerable to safety attacks like data replay and eavesdropping attacks. Node capture attacks function as destructive attacks that let attackers physically seize sensor nodes, reconfigure the structures, and deploy new nodes. An efficient architecture consists of a number of protocols for safe key creation and node capture attack revocation. A pairwise key establishment addresses arbitrary inputs from the pair of nodes implicated for the secure key establishment. Thus, the detailed exploration of various attack models to enhance key management security is a critical research direction in WSN security. Our model approaches the node capture attack problem from an attacker's viewpoint. The proposed model discovers the optimal collection of nodes likely to be attacked for node capturing. Based on the optimization algorithm i.e., fruit fly, the proposed model identifies multiple objectives like the set of dominating nodes, the vulnerability in paths, traveling cost, node contribution, and dominant rank and computes the optimal set of nodes with higher destructiveness. This indicates that the suggested node capture model has significant performance in the aspect of the least cost and lower attacking rounds. In this proposed model, we present an improved fruit fly optimization based attacking model consisting of several objectives as node strength, node and key participation rank, dominant rank and cost for capturing nodes in the system. Our approach outperforms existing attack models like RA, MLA, MTA, MKA, FGA, FFOA, and MA in terms of largest traffic compromised, lowest total attacking rounds, key captured, and least energy cost. The results demonstrated that the proposed method attained a path compromise probability up to 91% and reduced the cost by 60% in a network size of 100 nodes. The deduction in the network reliability up to 85% and throughput degrades by 30%.</p>

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Enhancing the Efficiency of Attacker with Improved Fruit Fly Optimization on Random Key Distribution for WSN

  • 期刊论文

  • 发表日期：

    2025年3月10日

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  • Rohit Bathla,
  • Priyanka Ahlawat

摘要

Wireless sensor networks (WSN) have seen immense use in everyday life, like health, battle-field administration, and disaster administration. Nodes inside WSN are more vulnerable to safety attacks like data replay and eavesdropping attacks. Node capture attacks function as destructive attacks that let attackers physically seize sensor nodes, reconfigure the structures, and deploy new nodes. An efficient architecture consists of a number of protocols for safe key creation and node capture attack revocation. A pairwise key establishment addresses arbitrary inputs from the pair of nodes implicated for the secure key establishment. Thus, the detailed exploration of various attack models to enhance key management security is a critical research direction in WSN security. Our model approaches the node capture attack problem from an attacker's viewpoint. The proposed model discovers the optimal collection of nodes likely to be attacked for node capturing. Based on the optimization algorithm i.e., fruit fly, the proposed model identifies multiple objectives like the set of dominating nodes, the vulnerability in paths, traveling cost, node contribution, and dominant rank and computes the optimal set of nodes with higher destructiveness. This indicates that the suggested node capture model has significant performance in the aspect of the least cost and lower attacking rounds. In this proposed model, we present an improved fruit fly optimization based attacking model consisting of several objectives as node strength, node and key participation rank, dominant rank and cost for capturing nodes in the system. Our approach outperforms existing attack models like RA, MLA, MTA, MKA, FGA, FFOA, and MA in terms of largest traffic compromised, lowest total attacking rounds, key captured, and least energy cost. The results demonstrated that the proposed method attained a path compromise probability up to 91% and reduced the cost by 60% in a network size of 100 nodes. The deduction in the network reliability up to 85% and throughput degrades by 30%.