Improved Ensemble Technique based on Support Vector Machine and Neural Network for Intrusion Detection System

  • Aafreen K. Siddiqui M.Tech Scholar, Department of CSE ASCT, BHOPAL
  • Tanveer Farooqui Assistant Professor Department of CSE , ASCT, BHOPAL
Keywords: Cyber-attack Classification, Ensemble Technique, SVM, NN, KDDCUP99, K-Nearest Neighbour.

Abstract

Intrusion Detection System (IDS) is a tool for anomaly detection in network that can help to protect network security. At present, intrusion detection systems have been developed to prevent attacks with accuracy Intrusion detection is a process for Cyber-attack classification and detection process is based on the fact that intrusive activities are different from normal system activities. Its detection is a very complex process in network security. In current network security scenario various types of cyber-attack family exist, some are known family and some are unknown one. The detection of known attack is not very difficult it generally uses either signature base approach or rule based approach, but to find out the unknown one is a challenging task. One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. This paper addresses using of an ensemble classification methods for intrusion detection. The paper proposes a cascaded support vector machine classifier or an improved ensemble classifier using multiple kernel function. The multiple kernel is Gaussian in nature. The graph based neural network technique used for feature collection of different types of cyber-attack data. The proposed algorithm is very efficient in comparison of pervious method.

Published
November 2017
How to Cite
Siddiqui, A. K., & Farooqui, T. (2017). Improved Ensemble Technique based on Support Vector Machine and Neural Network for Intrusion Detection System. INTERNATIONAL JOURNAL ONLINE OF SCIENCE, 3(12). Retrieved from http://ijoscience.com/ojsscience/index.php/ojsscience/article/view/9