1 research outputs found
Use Dimensionality Reduction and SVM Methods to Increase the Penetration Rate of Computer Networks
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 one of the major algorithms for intrusion detection. In
this research, we studied 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 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. The network
intrusion detection system is used by anomaly detection method that uses the
SVM algorithm for classification and SVD to reduce the size. 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.Also the KNN algorithm has been
compared in situations with and without diminishing dimensions,the results have
shown that the proposed method has a better performance than comparable
methods