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| 一种基于改进支持向量机的异常检测算法 | |
| 引用本文： | 詹琉. 一种基于改进支持向量机的异常检测算法[J]. 计算机与数字工程, 2021, 49(1): 158-162. DOI: 10.3969/j.issn.1672-9722.2021.01.032 |
| 作者姓名： | 詹琉 |
| 作者单位： | 广东工业大学 广州 511400 |
| 摘 要： | 为了进一步提高入侵检测的准确率,提出了一种融合半监督LDA和PSO-SVM方法,使用累计贡献率ω确定主成分分析法(PCA)占半监督LDA算法比例,然后使用PSO参数寻优算法对支持向量机进行参数寻优,最终得到入侵检测模型.实验结果显示,与单一的PCA或LDA与PSO-SVM组合相比,这种半监督LDA和PSO-SVM方法具... |
| 关 键 词： | PCA LDA 入侵检测 SVM 工业控制系统 |
An Anomaly Detection Method Based on Improved Support Vector Machine |
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| ZHAN Liu. An Anomaly Detection Method Based on Improved Support Vector Machine[J]. Computer and Digital Engineering, 2021, 49(1): 158-162. DOI: 10.3969/j.issn.1672-9722.2021.01.032 | |
| Authors: | ZHAN Liu |
| Affiliation: | (Guangdong University of Technology,Guangzhou 511400) |
| Abstract: | In order to further improve the accuracy of intrusion detection,a semi-supervised LDA and PSO-SVM method is proposed.The cumulative contribution rate is used to determine the proportion of principal component analysis(PCA)to semi-su?pervised LDA algorithm.The PSO parameter optimization algorithm is used to optimize the parameters of support vector machines,and finally the intrusion detection model is obtained.The experimental results show that this semi-supervised LDA and PSO-SVM method has advantages over the single PCA or LDA and PSO-SVM combination,and the accuracy of abnormal behavior is higher than that of PCA or LDA and PSO-SVM combination. |
| Keywords: | PCA LDA intrusion detection SVM industrial control system |
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