基于局部参数模型共享的分布式入侵检测系统
发布时间：2018-11-10 11:06
【摘要】：针对现有网络入侵检测系统(IDS)不能应对网络环境频繁变化的问题,提出一种基于局部参数模型共享的分布式网络IDS。在每个节点上根据网络连接数据,使用高斯混合模型(GMM)构建弱分类器,利用在线Adaboost算法对其进行优化,形成强分类器;将该节点上的GMM参数和强分类器参数组建成一个局部参数模型,并共享到其它节点;节点利用粒子群优化(PSO)寻找来自其它节点的最优局部参数模型,结合自身训练数据构建一个支持向量机(SVM)分类器,以此作为最终的全局检测器。实验结果表明,该IDS具有较高的检测率。
[Abstract]:In view of the problem that the existing network intrusion detection system (IDS) can not cope with the frequent changes in the network environment, a distributed network IDS. based on local parameter model sharing is proposed. According to the data of network connection on each node, a weak classifier is constructed by using Gao Si hybrid model (GMM), and the on-line Adaboost algorithm is used to optimize it to form a strong classifier. The GMM parameters and the strong classifier parameters on the node are constructed into a local parameter model and shared with other nodes. The node uses particle swarm optimization (PSO) (PSO) to find the optimal local parameter model from other nodes and constructs a support vector machine (SVM) classifier based on its training data as the final global detector. The experimental results show that the IDS has a high detection rate.
【作者单位】： 中国民航飞行学院科研处;
【基金】：国家自然科学基金民航联合基金重点项目(U1233202/F01)
【分类号】：TP393.08
，
本文编号：2322295
[Abstract]:In view of the problem that the existing network intrusion detection system (IDS) can not cope with the frequent changes in the network environment, a distributed network IDS. based on local parameter model sharing is proposed. According to the data of network connection on each node, a weak classifier is constructed by using Gao Si hybrid model (GMM), and the on-line Adaboost algorithm is used to optimize it to form a strong classifier. The GMM parameters and the strong classifier parameters on the node are constructed into a local parameter model and shared with other nodes. The node uses particle swarm optimization (PSO) (PSO) to find the optimal local parameter model from other nodes and constructs a support vector machine (SVM) classifier based on its training data as the final global detector. The experimental results show that the IDS has a high detection rate.
【作者单位】： 中国民航飞行学院科研处;
【基金】：国家自然科学基金民航联合基金重点项目(U1233202/F01)
【分类号】：TP393.08
，
本文编号：2322295
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