An Innovative and Efficient Approach for Detecting Unknown Attacks Using Feature Extraction Scheme and Fuzzy-Neural Networks

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2015 by IJCTT Journal
Volume-23 Number-2
Year of Publication : 2015
Authors : Vikas Belwal, Sandip Mandal
  10.14445/22312803/IJCTT-V23P114

MLA

Vikas Belwal, Sandip Mandal "An Innovative and Efficient Approach for Detecting Unknown Attacks Using Feature Extraction Scheme and Fuzzy-Neural Networks". International Journal of Computer Trends and Technology (IJCTT) V23(2):61-64, May 2015. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
In the past two decades, internet have experienced tremendous growth that has sped up a shift in computing environments from centralized computer systems to network information systems. A large volume of valuable information such as personal profiles, banking details and other valuable information is distributed and transferred through internet. Hence, network security has become a major concern than ever. Intrusion Detection Systems (IDSs) play a crucial role in detecting various kinds of attacks and defend our computer and data from them. Therefore, intrusion detection systems are effectively used for detecting intrusion accesses. Intrusion Detection Systems have been evolved over decades and various types of systems are currently available to identify and eradicate attacks based on different system conditions and different aptitudes. Many researchers have felt the importance of new techniques other than the ones which are currently uses. Towards this direction, data mining is considered to be very handy in achieving the desired results. Among many techniques fuzzy-neural networks (FNN) are very promising in this field. In this paper we are proposing a novel approach that uses Feature Extraction Scheme, Fuzzy-Neural Networks, K-means clustering and Support Vector Machines (SVM) for better results using Kyoto2006+ dataset.

References
[1] J.P.Anderson, “Computer security threat monitoring and surveillance,” James P. Anderson Co., Washington, 1980.
[2] R.Jang, “Neuro-Fuzzy modelling: Architecture, analysis and Application,” Ph.D Thesis, University of California, Berkley,1992.
[3] S.Mukkamala, G.I.Janoski, A.H.Sung, “Intrusion Detection Using Support Vector Machines,” in proceedings of the High Performance Computing Symposium- HPC, San Diego, CA, USA,2002, pp.178-183.
[4] “Kyoto2006+ dataset,” http://www.takakura.com/Kyoto data/.
[5] J. Song, H. Takakura, and Y. Kwon, “A generalized feature extraction scheme to detect 0-day attacks via ids alerts,” in Applications and the Internet, 2008. SAINT 2008. International Symposium on. IEEE, 2008, pp. 55–61.
[6] M.Sato, H.Yamaki, H.Takakura, “Unknown Attacks Detection Using Feature Extraction from Anomaly-based IDS Alerts,” 12th International Symposium on Applications and the Internet, IEEE/IPSJ, 2012.
[7] Gang Wang, Jinxing Hao, Jian Ma, Lihua Huang, “A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering,” Expert Systems with Applications, http://www.elsevier.com/locate/eswa/.
[8] A.M. Chandrasekhar, K.Raghuveer, “Intrusion Detection Technique using k-means, Fuzzy Neural Networks and SVM Classifiers,” in International Conference on Computer Communication and Informatics(ICCCI), IEEE, 2013.
[9] A.M. Chandrasekhar, K.Raghuveer, “Performance evaluation of data clustering techniqueusing KDD Cup99 intrusion dataset,” International Journal of Information and Network Security(IJINS), 2012.
[10] S.Revati, Dr. A.Malathi, “Intrusion Detection Based On Fuzzy Logic Approach Using Simplified Swarm Optimization”, International Journal of Computer Trends and Technology (IJCTT), 2014.
[11] Sugandha Gupta , Vandita Grover, “Survey of Intrusion Detection Techniques in LEACH”, International Journal of Computer Trends and Technology (IJCTT),2014

Keywords
Intrusion Detection System, Feature Extraction, Fuzzy-Neural Network, Support Vector Machines, K-means clustering.