改进的单类支持向量机的网络流量检测  

Internet traffic identification by using improved one class support vector machines

作　　者：吴旗[1] 刘健男[1] 寇文龙[1] 张宗升[1] 

机构地区：[1]吉林大学计算机科学与技术学院,长春130012

出　　处：《吉林大学学报（工学版）》2013年第S1期124-127,共4页Journal of Jilin University:Engineering and Technology Edition

摘　　要：单类支持向量机(OCSVM)理论对有限样本、高维空间和不平衡数据集分类有巨大优势,通过使用权重值模拟退火法与动态惯性因子的粒子群算法改进OCSVM的参数选择算法,进行流量分类,使得分类准确率提高了近10%,解决了传统流量分类方法的低准确率和开销大等弊端,对提高网络服务质量、网络管理与控制以及网络安全等领域具有重要意义。The theory of One Class Support Vector Machine(OCSVM) has an advantage over limited sample,high-dimensional space and unbalanced datasets.OCSVM parameter selection algorithm was improved by using a weight value simulated annealing method and dynamic inertia factor particle swam algorithm,as a result,the traffic classification accuracy is improved by nearly ten percent.Drawbacks such as low accuracy of the traditional traffic classification and overhead are solved.It is of great significance to improve the quality of network services,network management and control network security and so on.

关 键 词：流量分类 机器学习 支持向量机 参数选择 

分 类 号：TP393.06[自动化与计算机技术—计算机应用技术]