Authors:
Amir Moradibaad,Ramin Jalilian Mashhoud,DOI NO:
https://doi.org/10.26782/jmcms.2019.08.00002Keywords:
intrusion detection rate,computer networks,SVM,Abstract
In the world today computer networks have a very important position and most of the urban and national infrastructure as well as organizations are managed by computer networks, therefore, the security of these systems against the planned attacks is of great importance. Therefore, researchers have been trying to find these vulnerabilities so that after identifying ways to penetrate the system, they will provide system protection through preventive or countermeasures. SVM is considered as one of the major algorithms for intrusion detection. One of the major problems is the time of training and the need to improve its efficiency when it comes to work with large dimensions. In this research, we try to study a variety of malware and methods of intrusion detection, provide an efficient method for detecting attacks and utilizing dimension reduction. Thus, we will be able to detect attacks by carefully combining these two algorithms and pre-processes that are performed before the two on the input data. The main question raised in this study is how we can identify attacks on computer networks with the above-mentioned method. In anomalies diagnostic method, by identifying behavior as a normal behavior for the user, the host, or the whole system, any deviation from this behavior is considered as an abnormal behavior, which can be a potential occurrence of an attack. In this research, the network intrusion detection system is used by anomaly detection method that uses the SVM algorithm for classification and SVD to reduce the size. The various steps of the proposed method include pre-processing of the data set, feature selection, support vector machine, and evaluation. The NSL-KDD data set has been used to teach and test the proposed model. In this study, we inferred the intrusion detection using the SVM algorithm for classification and SVD for diminishing dimensions with no classification algorithm. And also the KNN algorithm has been compared in situations with and without diminishing dimensions and the results have shown that the proposed method has a better performance than comparable methods.Refference:
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