An Anomaly Detector Deployment Awareness Detection Framework Based on Multi-Dimensional Resources Balancing in Cloud Platform

Li, Jun; Zhang, Hancui; Xu, Guangxia

doi:10.1109/access.2018.2865114

Cited by 5 publications

(1 citation statement)

References 24 publications

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“…Currently, there are many algorithms that can be used to anomaly detection. Liu et al proposed an anomaly detector deployment awareness detection framework based on multi-dimensional resources balance, and experiments show that this framework achieves a higher scalability and detection accuracy [3]. Shakya et al show a weighted hybrid model utilizing Support Vector Machine and Naive Bayes for anomaly discovery [4].…”

Section: Power System Anomaly Detection Based On Ocsvmmentioning

confidence: 99%

Power System Anomaly Detection Based on OCSVM Optimized by Improved Particle Swarm Optimization

Wang

Song

et al. 2019

IEEE Access

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This paper tries to solve anomaly detection, a very important issue in ensuring the safe and stable operation of power system. As the proportion of abnormal data in the operation of power system is very small, a one-class support vector machine (OCSVM) is adopted in this paper, which is suitable for classification of unbalanced data. However, the performance of OCSVM is sensitive to its parameters, and an unsuitable choice will decrease the classification accuracy and generalization ability of it. In this paper, particle swarm optimization (PSO) is used to optimize the parameters of OCSVM. The original PSO algorithm converges slowly and easily falls into local optimum. To overcome this issue, this paper proposes an improved PSO algorithm for parameters optimization, in which adaptive speed weighting and adaptive population splitting are introduced to improve the convergence speed of the algorithm and help the algorithm jump out of the local optimal position. Experiments on standard benchmarks and real power system experimental data sets demonstrate the effectiveness of the proposed algorithm.

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Section: Power System Anomaly Detection Based On Ocsvmmentioning

confidence: 99%