A Review on Intelligent Data Mining and Soft Computing Techniques for Effective Intrusion Detection

Authors(2) :-Suma S G, Dr. Ganapathy Sannasi

The world of computer today is increasingly dependent on interconnections between computer systems. Internet usage is increasing rapidly that the security against real time attack is a major concern. An intrusion detection system is a key defensive mechanism against the network attacks. Various approaches to intrusion detection are being used currently. This paper discusses about the various feature selection and intelligent classification techniques that are used to detect intrusions effectively. In addition to this, intrusion detection systems based on intelligent soft computing techniques are also discussed. Finally, a new intrusion detection model based on artificial immune system and fuzzy rough set based feature selection is suggested for effective and dynamic intrusion detection.

Authors and Affiliations

Suma S G
SCSE, VIT, Chennai, Tamil Nadu, India
Dr. Ganapathy Sannasi
SCSE, VIT, Chennai, Tamil Nadu, India

Intrusion Detection System, Neural Networks, Artificial Immune System, Fuzzy System, Particle Swarm Optimization

  1. William Stallings, "Cryptography and Network Security Principles and Practices", Prentice Hall, Upper Saddle River, 2006.
  2. Krishna Kant Tiwari, Susheel Tiwari, Sriram Yadav, "Intrusion Detection Using Data Mining Techniques", International Journal of Advanced Computer Technology, Vol.2, No. 4, pp.1-3, 2010.
  3. Sagar S.Nikam, "A Comparative Study of Classification Techniques in Data Mining Algorithms", Oriental Journal of Computer Science and Technology, Vol.8, No.1, pp.13-19.
  4. Sannasi Ganapathy, Kanagasabai Kulothungan, Sannasy Muthurajkumar, Muthusamy Vijayalakshmi, Palanichamy Yogesh and Arputharaj Kannan, " Intelligent feature selection and classification techniques for intrusion detection in networks:a survey", EURASIP Journal on Wireless Communications and Networking, Vol.271, pp.1-16, 2013.
  5. S Chebrolu, A Abraham, P Johnson, Thomas, "Feature deduction and ensemble design of intrusion detection systems", Computers & Security, Vol.24, No.4, pp.295-307, 2005.
  6. W Zhang, S Teng, H Zhu, H Du, X Li, "Fuzzy Multi-Class Support Vector Machines for Cooperative Network Intrusion detection", Proc.9th IEEE Int.Conference on Cognitive Informatics (ICCI’10), pp.811-818, 2010.
  7. L Zadeh, "Role of soft computing and fuzzy logic in the conception, design and development of information/intelligent systems, in Computational Intelligence:Soft Computing and Fuzzy-Neuro Integration with Applications", Proceedings of the NATO Advanced Study Institute on Soft Computing and its Applications, Vol.162, pp.1-9, 1998.
  8. SS Sivatha Sindhu, S Geetha, A Kannan, "Decision tree based light weight intrusion detection using a wrapper approach", Expert Syst.Applications, Vol.39, pp.129-141, 2012.
  9. Basant Subba , Santosh Biswas, Sushanta Karmakar , "A Neural Network Based System for Intrusion Detection and Attack Classification", Twenty Second National Conference on Communication (NCC), pp.1-6, 2016 .
  10. Ishibuchi, H and Yamamoto, T."Rule weight specification in fuzzy rule-based classification systems", IEEE Transactions on Fuzzy Systems, Vol.13, pp.428-435, 2005.
  11. Salma Elhag, Alberto Fernande, Abdullah Bawakid , Saleh Alshomrani , Francisco Herrera"On the combination of genetic fuzzy systems and pairwise learning for improving detection rates on Intrusion Detection Systems", Expert Systems with Applications, Vol.42, pp.193-202, 2015.
  12. Cordon, O., Gomide, F., Herrera, F., Hoffmann, F., & Magdalena, L, "Ten years of genetic fuzzy systems:Current framework and new trends.Fuzzy Sets and Systems", Vol.141, No.1, pp.5-31, 2004.
  13. Hastie, T., & Tibshirani R, "Classification by pairwise coupling", The Annals of Statistics, Vol.26, No.2, pp.451-471, 1998.
  14. Alcala-Fdez, J., Alcala, R., & Herrera, F, "A fuzzy association rule-based classification model for high-dimensional problems with genetic rule selection and lateral tuning", IEEE Transactions on Fuzzy Systems, Vol.19, No.5, pp.857-872, 2011.
  15. Seyed Mojtaba Hosseini Bamakan, Huadong Wang, Tian Yingjie , Yong Shi , "An effective intrusion detection framework based on MCLP/SVM optimized by time-varying chaos particle swarm optimization", Neurocomputing, Vol.199, pp.90-102, 2016.
  16. Ketan Sanjay Desale, Roshani Ade"Genetic Algorithm based Feature Selection Approach for Effective Intrusion Detection System", International Conference on Computer Communication and Informatics, pp.1-6, 2015.
  17. Soo-Yeon Ji, Bong-Keun Jeong, Seonho Choi, Dong Hyun Jeong, "A multi-level intrusion detection method for abnormal network behaviours", Journal of Network and Computer Applications, Vol.62, pp.9-17, 2016.
  18. Ognjen Joldzic, Zoran Djuric, Pavle Vuletic, "A transparent and scalable anomaly-based DoS detection method", Computer Networks, Vol.104, pp.27-42, 2016.
  19. Omar Y.Al-Jarrah, Omar Alhussein, Paul D.Yoo, Sami Muhaidat, Kamal Taha, Kwangjo Kim, "Data Randomization and Cluster-Based Partitioning for Botnet Intrusion Detection", IEEE Transactions on Cybernetics, Vol.46, No.8, pp.1797-1801, 2016.
  20. Abdulla Amin Aburomman, Mamun Bin Ibne Reaz, " A novel SVM-kNN-PSO ensemble method for intrusion", Applied Soft Computing, Vol.38, pp.360-372, 2015.
  21. Parham Moradi, Mehrdad Rostami, "Integration of graph clustering with ant colony optimization for feature selection", Knowledge-Based Systems, Vol.84, pp.144-161, 2015.
  22. Alatas, B., Akin, E., 2005."Mining fuzzy classification rules using an artificial immune system with boosting", Adv.Databases Inf.Syst.3631, pp.283-293, 2005.
  23. Meng-Hui Chen, Pei-Chann Chang, Jheng-Long Wu, "A population-based incremental learning approach with artificial immune system for network intrusion detection", Engineering Applications of Artificial Intelligence, Vol. 51, pp. 171-181, 2016.

Publication Details

Published in : Volume 2 | Issue 6 | November-December 2017
Date of Publication : 2017-12-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 805-814
Manuscript Number : CSEIT1726216
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Suma S G, Dr. Ganapathy Sannasi, "A Review on Intelligent Data Mining and Soft Computing Techniques for Effective Intrusion Detection", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 6, pp.805-814, November-December-2017. |          | BibTeX | RIS | CSV

Article Preview