Abstract

To improve network security different steps has been taken as size and importance of the network has increases day by day. In order to find intrusion in the network IDS systems were developed. In this paper main focus was done on finding the type of session i.e. normal or intrusion where if intrusion found than class of intrusion was detected. Here whole work was so designed that automatic clustering of various sessions are done by using genetic algorithm steps while clustered data is taken as the input in the neural network for training. So, the need of special identification was required in this work for session class. Error back propagation neural network was used by this work training and testing. Experiment was done on real dataset where various set of testing data was pass for comparison on different evaluation parameters.

References

• Yogita B. Bhavasar, Kalyani C. Waghmare “Intrusion Detection System Using Data Mining Technique: Support Vector Machine” 2013 International Journal of Emerging Technology and Advance Engineering volume 3, Issue 3, March 2013.
• A.R. Jakhale, G.A. Patil, “Anomaly Detection System by Mining Frequent Pattern using Data Mining Algorithm from Network Flow”, International Journal of Engineering Research and Technology, Vol. 3, No.1, January 2014, ISSN. 2278-0181.
• Aljurayban, N.S.; Emam, A. (21-23 March 2015). Framework for Cloud Intrusion Detection System Service. Web Applications and Networking (WSWAN), 2015 2nd World Symposium on, p1-5
• Barolli, Leonard; Elmazi, Donald; Ishitaki, Oda, Tetsuya; Taro; Yi Liu, Uchida, Kazunori. (24-27 March 2015). Application of Neural Networks for Intrusion Detection in Tor Networks. Advanced Information Networking and Applications Workshops (WAINA), 2015 IEEE 29th International Conference on, p67-72.
• Koushal Kumar, Jaspreet Singh Batth “Network Intrusion Detection with Feature Selection Techniques using Machine-Learning Algorithms” International Journal of Computer Applications (0975 – 8887) Volume 150 – No.12, September 2016.
• R.Karthik, Dr.S.Veni, Dr.B.L.Shivakumar “Improved Extreme Learning Machine (IELM) Classifier For Intrusion Detection System” International Journal of Engineering Trends and Technology (IJETT) – Volume-41 Number-2 - November 2016.
• Premansu sekhara rath, 2manisha mohanty, 3silva acharya, 4monica aich “optimization of ids algorithms using data mining technique” International Journal of Industrial Electronics and Electrical Engineering, ISSN: 2347-6982 Volume-4, Issue-3, Mar.-2016
• Mohammadreza Ektefa, Sara Memar, Fatimah Sidi, Lilly Suriani Affendey “Intrusion Detection Using Data Mining Techniques”, 978-1-4244-5651-2/10/$26.00 ©2010 IEEE
• YU-XIN MENG,” The Practice on Using Machine Learning For Network Anomaly Intrusion Detection” Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong, 978-1-4577-0308-9/11/$26.00 ©2011 IEEE
• Liu Hui, CAO Yonghui “Research Intrusion Detection Techniques from the Perspective ofMachine Learning”2010 Second International Conference on MultiMedia and Information Technology 978-0-7695-4008-5/10 $26.00 © 2010 IEEE
• Chuanlong Yin , Yuefei Zhu, Jinlong Fei, And Xinzheng He. “A Deep Learning Approach For Intrusion Detection Using Recurrent Neural Networks” current version November 7,2017 .Digital Object Identifier
• NSL-KDD dataset source:https://github.com/defcom17/NSL_KDD/blob/master/Original%20NSL%20KDD%20Zip.zip