Parallel Charged System Search Algorithm for Energy Management in Wireless Sensor Network

Jiang, Tong-Bang; Chu, Shu-Chuan; Pan, Jeng‐Shyang

doi:10.1109/iai50351.2020.9262194

Cited by 3 publications

(1 citation statement)

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“…w ij ∈ [0, 1], the random number represents the j − th distance weight in the i − th solution. Evolutionary algorithm will continuously search and iterate under the guidance of the objective function and finally output the optimal solution or the approximate best [48][49][50][51], that is, the most suitable related parameters of kNN.…”

Section: Combination Of Pm-csca and Knn Knn Parameter Kmentioning

confidence: 99%

A Lightweight Intelligent Intrusion Detection Model for Wireless Sensor Networks

Pan

Fang

Chu

et al. 2021

Security and Communication Networks

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The wide application of wireless sensor networks (WSN) brings challenges to the maintenance of their security, integrity, and confidentiality. As an important active defense technology, intrusion detection plays an effective defense line for WSN. In view of the uniqueness of WSN, it is necessary to balance the tradeoff between reliable data transmission and limited sensor energy, as well as the conflict between the detection effect and the lack of network resources. This paper proposes a lightweight Intelligent Intrusion Detection Model for WSN. Combining k-nearest neighbor algorithm (kNN) and sine cosine algorithm (SCA) can significantly improve the classification accuracy and greatly reduce the false alarm rate, thereby intelligently detecting a variety of attacks including unknown attacks. In order to control the complexity of the model, the compact mechanism is applied to SCA (CSCA) to save the calculation time and space, and the polymorphic mutation (PM) strategy is used to compensate for the loss of optimization accuracy. The proposed PM-CSCA algorithm performs well in the benchmark functions test. In the simulation test based on NSL-KDD and UNSW-NB15 data sets, the designed intrusion detection algorithm achieved satisfactory results. In addition, the model can be deployed in an architecture based on cloud computing and fog computing to further improve the real-time, energy-saving, and efficiency of intrusion detection.

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Section: Combination Of Pm-csca and Knn Knn Parameter Kmentioning

confidence: 99%