摘要
为了提高网络入侵检测的正确率,提出一种基于组合算法选择特征的网络入侵检测模型(GA-PSO)。首先建立网络入侵特征选择的数学模型,采用遗传算法迅速找到网络入侵的特征子集,然后采用粒子群算法进一步选择,找到最优特征子集,最后采用极限学习机建立网络入侵检测分类器,并采用KDD CUP 99数据集进行仿真测试。结果表明,GAPSO不仅提高了入侵检测速度,而且可以提高网络入侵检测的正确率。
In order to improve the detection accuracy of network intrusion,this paper proposed a network intrusion detection model based on combination algorithm selecting features. Firstly,the mathematical model of network intrusion detection features selecting problem is established,and then genetic algorithm is used to find the feasible sub-features,and the optimal sub-features is obtained by particle swarm optimization algorithm,finally,the network intrusion detection model is established by relevance vector machine,and the performance is test by simulation experiments. The test results show that the proposed model can not only improved the detection speed,but also can improve the network intrusion detection accuracy.
出处
《计算机与现代化》
2014年第8期75-80,共6页
Computer and Modernization
关键词
特征选择
入侵检测
遗传算法
粒子群优化算法
feature selection
intrusion detection
genetic algorithm
particle swarm optimization algorithm
