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Volume 7 Issue 6
Oct.  2020

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2020  >  7(6): 1627-1637
Jinyin Chen, "A Novel Radius Adaptive Based on Center-Optimized Hybrid Detector Generation Algorithm," IEEE/CAA J. Autom. Sinica, vol. 7, no. 6, pp. 1627-1637, Nov. 2020. doi: 10.1109/JAS.2018.7511192
Citation: Jinyin Chen, "A Novel Radius Adaptive Based on Center-Optimized Hybrid Detector Generation Algorithm," IEEE/CAA J. Autom. Sinica, vol. 7, no. 6, pp. 1627-1637, Nov. 2020. doi: 10.1109/JAS.2018.7511192
shu

A Novel Radius Adaptive Based on Center-Optimized Hybrid Detector Generation Algorithm

doi: 10.1109/JAS.2018.7511192

  • The College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China

Funds:

the National Natural Science Foundation of China 61502423

the National Natural Science Foundation of China 62072406

the Natural Science Foundation of Zhejiang Provincial LY19F020025

the Major Special Funding for "Science and Technology Innovation 2025" in Ningbo 2018B10063

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    Abstract
  • Negative selection algorithm (NSA) is one of the classic artificial immune algorithm widely used in anomaly detection. However, there are still unsolved shortcomings of NSA that limit its further applications. For example, the nonself-detector generation efficiency is low; a large number of nonself-detector is needed for precise detection; low detection rate with various application data sets. Aiming at those problems, a novel radius adaptive based on center-optimized hybrid detector generation algorithm (RACO-HDG) is put forward. To our best knowledge, radius adaptive based on center optimization is first time analyzed and proposed as an efficient mechanism to improve both detector generation and detection rate without significant computation complexity. RACO-HDG works efficiently in three phases. At first, a small number of self-detectors are generated, different from typical NSAs with a large number of self-sample are generated. Nonself-detectors will be generated from those initial small number of self-detectors to make hybrid detection of self-detectors and nonself-detectors possible. Secondly, without any prior knowledge of the data sets or manual setting, the nonself-detector radius threshold is self-adaptive by optimizing the nonself-detector center and the generation mechanism. In this way, the number of abnormal detectors is decreased sharply, while the coverage area of the nonself-detector is increased otherwise, leading to higher detection performances of RACO-HDG. Finally, hybrid detection algorithm is proposed with both self-detectors and nonself-detectors work together to increase detection rate as expected. Abundant simulations and application results show that the proposed RACO-HDG has higher detection rate, lower false alarm rate and higher detection efficiency compared with other excellent algorithms.

     

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    Highlights

    • In RACO-HDG, phase generation is designed for self-detector by using a small number of self-detector instead of a large number of anomaly detectors.
    • In order to achieve optimal radius for each detector, self-adapt radius threshold is put forward based on self samples distribution.
    • For reducing size of nonself-detector, nonself-detectors are generated from far to near.
    • RACO-HDG reduces the number of self-detector and anomaly detectors to 1/10, and increases the true negative rate and reducing the false alarm rate.

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