Empirical evolution of spider and grey wolf optimization techniques on svm classifier for intrusion detection in high traffic networks

Abstract

The network traffic data analysis plays an essential role in the information and newlinecommunication system. Due to the occurrence of intrusion incidents in the network, newlinethe providing of security to the network has a high level of task significance. Network newlinesecurity is ensured by protecting the data from unauthorized access through the newlinedetection of attacks on the data. Intrusion Detection System (IDS) is an essential newlinetechnology to ensure security with the identification of intrusions on the network by newlinemonitoring the network traffic continuously. Accordingly, a lot of research works newlinehave been done with IDS for detecting legal and illegal users. However, existing newlinemethods failed to address the issues of non-linear classification and multi-class cases. newlineBesides, these methods consider only the continuous features and have not taken into newlineaccount the categorical features. To overcome these limitations, the research work newlinedevelops three proposed techniques that perform intrusion detection with a higher newlineconvergence rate at a faster running time. newline newline