摘要
为了提高网络入侵检测的正确率,提出一种基于KNN-IPSO选择特征的网络入侵检测模型(KNN-IPSO)。首先采用K近邻算法消除原始网络数据中的冗余特征,并将其作为粒子群算法的初始解,然后采用粒子群算法找到最优特征子集,并对粒子的惯性权重进行自适应调整和种群进行混沌操作,帮助种群跳出局部最优,最后采用KDD CUP 99数据集对KNN-IPSO的性能进行测试。结果表明,KNN-IPSO消除了冗余特征,降低了分类器的输入维数,有效提高了入侵检测正确率和检测速度。
In order to improve the detection accuracy of network intrusion, this paper proposes a novel network intrusion detection model（KNN-IPSO）based on K Nearest Neighbor and Improved Particle Swarm Optimization algorithm to select the features. Firstly, KNN algorithm is used to eliminate redundant features and remove the redundant features of network data and the selected features are taken as the initial solution of PSO, secondly, the optimal features are obtained by PSO which inertia weight is adjusted adaptively and chaotic system is intruded to help the swarms jump out the local optimal solutions, finally the KDD CUP 99 data sets are used to test the performance of KNN-IPSO. The results show that the pro-posed model can eliminate the redundant features and reduce the input dimensions of classifier. It can improve the net-work intrusion detection accuracy and detection speed.
出处
《计算机工程与应用》
CSCD
2014年第17期95-99,共5页
Computer Engineering and Applications
基金
安徽省高等学校省级自然科学研究项目(No.KJ2013B206)
安徽省高校省级优秀青年人才基金重点项目(No.2013SQRL102ZD)
关键词
入侵检测
特征选择
特征关联性
改进粒子群算法
intrusion detection
feature selection
feature relevance
improved particle swarm optimization algorithm
