The week's pick
Random Articles
Reseach Article
Performance Analysis of Intrusion Detection Systems Implemented using Hybrid Machine Learning Techniques
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 133 - Number 8 |
| Year of Publication: 2016 |
| Authors: Purushottam R. Patil, Yogesh Sharma, Manali Kshirasagar |
10.5120/ijca2016907997
|
Purushottam R. Patil, Yogesh Sharma, Manali Kshirasagar . Performance Analysis of Intrusion Detection Systems Implemented using Hybrid Machine Learning Techniques. International Journal of Computer Applications. 133, 8 ( January 2016), 35-38. DOI=10.5120/ijca2016907997
Abstract
Intrusion Detection System (IDS) are said to be more effective when it has both high intrusion detection (true positive) rate and low false alarm (false positive). But current IDS when implemented using data mining approach like clustering, classification alone are unable to give 100 % detection rate hence lack effectiveness. In order to overcome these difficulties of the existing systems, many researchers implemented intrusion detection systems by integrating clustering and classification approach like k-means and Fuzzy logic, K-means and genetic algorithm, some of the researcher also tried use of Decision tree and Neural Network to detect unknown attacks. In this paper analysis of such Hybrid systems which are implemented by using the benchmark dataset compiled for the 1999 KDD intrusion detection contest, by MIT Lincoln Labs.
References
- Myung-Sup Kim, Hun-Jeong Kang, Seong-Cheol Hong, Seung-Hwa Chung, and James W. Hong 2003 “A Flow-based Method for Abnormal Network Traffic Detection”,
- CNN, Cyber-attacks batter web heavyweights, February 2000,http://www.cnn.com/2000/TECH/computing/02/09cyber.attacks.01Drew Dean, Matt Franklin, and Adam Stubblefield
- 2001. “An algebraic approach to IP trace back,” Proc. of Network and Distributed System Security Symposium, NDSS '01, San Diego, California.
- L. John Ioannidis and Steven M. Bellovin February 2002 ,“Implementing pushback: Router-based defense against DDoS attacks,” Proc. of Network and Distributed System Security Symposium, NDSS '02, San Diego, California.
- Stefan Savage, David Wetherall, Anna Karlin, and Tom Anderson 2000 “Practical network support for IP traceback,” Proc. of the 2000 ACM SIGCOMM, Stockholm, Sweden.
- Kun-chan Lan, Alefiya Hussain, and Debojyoti Dutta April 2003, “Effect of Malicious Traffic on the Network,” Proc. of PAM 2003, San Diego, California,.
- Se-Hee Han, Myung-Sup Kim, Hong-Taek Ju, and James W. Hong 2002 “The Architecture of NG-MON: A Passive Network Monitoring System,” Lecture Notes in Computer Science 2506, 13th IFIP/IEEE International Workshop on Distributed Systems: Operations and Management (DSOM 2002), Montreal, Canada.
- Dewan Md. Farid and Mohammad Zahidur Rahman 2010, “Anomaly Network Intrusion Detection Based on Improved Self Adaptive Bayesian Algorithm”, Journal Of Computers, Vol. 5, No. 1.
- Sampada Chavan, Khusbu Shah, Neha Dave, Sanghan Mitra Mukherjee, Ajith Abraham and Sugata Sanyal 2004. ”Adaptive Neuro-Fuzzy Intrusion detection Systems”,ITCC’04, IEEE.
- Srilatha Chebrolu, Ajith Abraham and Johnson P. Thomas 2005,” Feature deduction and ensemble design of intrusion detection systems”, Computers & Security.
- Muna M. Taher Jawhar and Monica Mehrotra January-June 2010,“Anomaly Intrusion Detection System using Hamming Network Approach”, International Journal of Computer Science & Communication, Vol. 1, No. 1.
- Dong Song, Malcolm I. Heywood, Nur Zincir-Heywood 2005,“Training Genetic Programming on Half a Million Patterns: An Example From Anomaly Detection, IEEE transactions on evolutionary computation,Vol. 9, No. 3.
- Adel Nadjaran Toosi and Mohsen Kahani, 2007 “A New approach to intrusion detection based on an evolutionary soft computing model using neuro-fuzzy classifiers”, Elsevier B.V.
- Preeti Aggarwal,Sudhir Kumar Sharma, 2015 ”Analysis of KDD Dataset Attributes-class wise For Intrusion Detection”,Procedia Computer Science, Elsevier
- [K. M. Faraoun and A. Boukelif, 2005 “Neural Networks Learning Improvement using the K-Means Clustering Algorithm to Detect Network Intrusions” International Journal of Computational Intelligence” Vol. 3 .
- Ajith Abrahama, Ravi Jain, Johnson Thomas, Sang Yong Hana 2007 ” D-SCIDS: Distributed soft computing intrusion detection system” Journal of Network and Computer Applications, pp.81–98.
- Alireza Osareh, Bita Shadgar 2008,” Intrusion Detection in Computer Networks based on Machine Learning Algorithms”, IJCSNS International Journal of Computer Science and Network Security, Vol.8, No.11,
- Marjan Bahrololum, Elham Salahi and Mahmoud Khaleghi, December 2009 “An Improved Intrusion Detection Approach based on two Strategies Using Decision Tree and Neural Network, ”Journal of Convergence Information Technology Vol. 4, No. 4.
- Mr. Vivek A. Patole, Mr. V. K. Pachghare and Dr. Parag Kulkarni 2010 ,“Self Organizing Maps to Build Intrusion Detection System”, International Journal of Computer Applications.
- Jiawai Han and Mitcheline Kamber 2006,”Data Mining Concepts and approachs”,2e,Elsevier.
- .Hai Nguyen, Katrin Franke and Slobodan Petrovic 2010, ” Improving Effectiveness of Intrusion Detection by Correlation Feature Selection”,IEEE
- Jongsuebsuk P, Wattanapongsakorn N Charnsripinyo C. 2013,” Real-time intrusion detection with fuzzy genetic algorithm”, 10th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), IEEE.
- Sharmila Kishor Wagh, Dr.Satish R. Kolhe 2014, ”Effective Intrusion Detection System Using SemiSupervised Learning”, IEEE.
- Samaneh Rastegari, Chiou-Peng Lam, Philip Hingston 2015 ,“A Statistical Rule Learning Approach to Network Intrusion Detection”, IEEE.
- HaiJiang Steven Shi 2005,” Model-based Clustering” University of Waterloo, Canada,.
Index Terms
Keywords