文献类型：期刊文献

中文题名：基于CUDA和布谷鸟算法的SVM在工控入侵检测中的应用

英文题名：Application of CUDA and Cuckoo Algorithm Based SVM in Industrial Control System Intrusion Detection

作者：陈汉宇[1];王华忠[1];颜秉勇[1]

机构：[1]华东理工大学化工过程先进控制和优化技术教育部重点实验室,上海200237

年份：2019

卷号：45

期号：1

起止页码：101

中文期刊名：华东理工大学学报（自然科学版）

外文期刊名：Journal of East China University of Science and Technology

收录：CSTPCD;;Scopus;北大核心:【北大核心2017】；CSCD:【CSCD_E2019_2020】；

基金：国家自然科学基金青年基金(51407078)

语种：中文

中文关键词：布谷鸟搜索算法;CUDA;支持向量机;工业控制系统;入侵检测

外文关键词：cuckoo search algorithm;CUDA;SVM;industrial control system;intrusion detection

摘要：为了提升SVM算法的分类速度和精度,提出了一种基于CUDA和布谷鸟搜索算法(CSA)的CCS-SVM (CUDA and Cuckoo Search based Support Vector Machine)算法。考虑到SVM算法在大规模数据下训练速度慢的缺点,利用基于CUDA的并行技术对SVM进行并行化。针对布谷鸟搜索算法寻优精度低和收敛速度慢的问题,提出了两点改进:第一,考虑了寻优过程中个体适应度对莱维飞行步长因子α的影响;第二,在偏好随机游动环节引入惯性权重。最后利用CCS-SVM算法对工控网络标准数据集进行入侵检测仿真实验,结果表明:该算法在保证入侵检测准确率的同时,检测速度提升了近3倍。
By using the compute unified device architecture(CUDA)and cuckoo search algorithm(CSA),this paper proposes an improved support vector machine algorithm,termed as CCS-SVM,to improve the classify speed and accuracy of classical SVM algorithm.SVM is one of the most popular classifiers for the classification process,but its training is very computationally intensive for large scale data,scales badly with the size of the data sets.Aiming at the slow training speed of SVM in large-scale data,this paper proposes a parallel SVM algorithm to accelerate training process by means of CUDA technology.Especially,the computations of SVM kernel function and the parallel SMO algorithm are implemented on the GPU.The classification accuracy of SVM is closely related to the setting of kernel function parameters,e.g.,gamma(γ)for the radial basis function(RBF)kernel and penalty parameter C.In this work,CSA will be utilized to optimize these parameters.CSA is a recently developed meta-heuristic optimization algorithm and is quite suitable for solving optimization problems.The traditional CSA uses two fixed value parameters pα and α,which may degrade its performance and increase the iteration times under the case that pα is small and α is large.Moreover,CSA may not find the optimal solutions,although its convergence speed may be very fast.In order to overcome the above shortcoming,this paper makes two improvements.Firstly,the influence of individual fitness on step size factor α is considered during the optimization process.Secondly,the inertia weight is introduced into the random walk.Finally,the proposed CCS-SVM algorithm is verified via the intrusion detection simulation experiment on industrial network standard dataset,which shows that the proposes algorithm can improve the detection speed by almost 3 times while guaranteeing the accuracy of intrusion detection.