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.
Keywords:
intrusion detection rate,computer networks,SVM,
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