Detection of network penetration by data mining and using machine learning via SVM algorithm

Computer networks are spreading widely and one of the most outstanding challenges in computer network security is detecting intrusions into networks. One of the main tools for detection is controlling network traffic and analyzing users’ behavior. One way of accomplishing this is to set classifications that specify the patterns in huge volumes of data. By means of data mining methods and introducing a binary label (normal pack, abnormal pack) and specifying the priority of data, abnormal data is detected leading to increased accuracy of network intrusion detection which in turn leads to improvement and maintenance of network security. In this paper, SVM algorithm is analyzed in terms of priorities and the effect of machine learning algorithm on accuracy of intrusion detection is investigated. The results show that using SVM is more advantageous compared to past approaches yielding better detection and increasing accuracy and right alarm detection.