Intrusion Detection In Computer Networks Research Articles

Fusion of multiple classifiers for intrusion detection in computer networks

The security of computer networks plays a strategic role in modern computer systems. In order to enforce high protection levels against threats, a number of software tools have been currently developed. Intrusion Detection Systems aim at detecting intruders who elude “first line” protection. In this paper, a pattern recognition approach to network intrusion detection based on the fusion of multiple classifiers is proposed. Five decision fusion methods are assessed by experiments and their performances compared. The potentialities of classifier fusion for the development of effective intrusion detection systems are evaluated and discussed.

Simulation Research on Optimal Detection of Intrusion Node Information in Network

The optimal detection of intrusion node information in the network can guarantee the safe and stable operation of the network. When the intrusion node information is detected, it is necessary to obtain the optimal parameters of SVM according to the optimal acquisition path of the node to complete the detection of intrusion node information. The traditional method uses the ant colony to find the network node path, get the support vector machine parameters, but ignores the optimization of the parameters, resulting in the information detection results are not accurate. An improved detection method of intrusion node information based on attribute attack graph is proposed. The intrusion signal with intrusion node information is decomposed into IMF single frequency intrusion signal, and the state transition equation of the network intrusion detection system is obtained. The ant colony theory and the support vector machine parameters are merged, and the network intrusion check rate is used as the objective function, and the ant is changed to the ant, and the nodes on the optimal path are connected to get the SVM optimal parameters. Based on this parameter, the intrusion node information detection is completed. The experimental results show that the proposed method has high accuracy and can improve the accuracy of embedded computer network intrusion detection.