Home
Call For Papers
Submission
Author
Registration
Publications
About
Contact Us

  A Review on Hybrid Intrusion Detection System Using TAN & SVM  
  Authors : Sumalatha Potteti; Namita Parati
  Cite as:

 

The dramatically development of internet, Security of network traffic is becoming a major issue of computer network system. Attacks on the network are increasing day-by-day. The Hybrid framework would henceforth, will lead to effective, adaptive and intelligent intrusion detection. In this paper, We propose a hybrid fuzzy rough with Naive bayes classifier, Support Vector Machine and K-nearest neighbor (K-NN) based classifier (FRNN) to classify the patterns in the reduced datasets, obtained from the fuzzy rough bioinspired algorithm search. The proposed hybrid is subsequently validated using real-life datasets obtained from the University of California, Irvine machine learning repository. Simulation results demonstrate that the proposed hybrid produces good classification accuracy. Finally, parametric and nonparametric statistical tests of significance are carried out to observe consistency of the classifiers.

 

Published In : IJCSN Journal Volume 4, Issue 3

Date of Publication : June 2015

Pages : 475 - 481

Figures : 03

Tables : 01

Publication Link : A Review on Hybrid Intrusion Detection System Using TAN & SVM

 

 

 

Namita Parati : is working as Assistant Professor at Bhoj Reddy Engineering College for Women, Hyderabad, INDIA. She has received B.E, M.Tech Degree in Computer Science and Engineering. Her main research interest includes intrusion detection using hybrid network.

Sumaltha Potteti : is working as Assistant Professor at Bhoj Reddy Engineering College for Women, Hyderabad, INDIA. She has received B.Tech, M.Tech Degree in Computer Science and Engineering. Her main research interest includes Cloud computing and intrusion detection

 

 

 

 

 

 

 

Intrusion Detection System (IDS)

Data Mining

Classification

Support vector machines (SVM)

K-Nearest Neighbor (KNN)

Naive Bayes Classifier

This paper proposes an envisioning framework for intrusion detection i.e. Hybrid Intrusion Detection System. The developed framework is an intelligent, adaptive and effective intrusion detection framework. The experimental analysis is performed on the developed IDS framework and is compared with other techniques present in the scenario. The resultants obtained convey that the developed hybrid framework is highly effective to overcome the deficiencies found in previous work. As the framework uses two data mining techniques (i.e. TAN and SVM) to breed the classification rules, it can be effortlessly implemented in real time and is able to detect and adapt new types of intrusive activities. Also experimental assessment shows that the developed framework has reduced the false alarm rate and increased the accuracy up to noteworthy extend which is a major concern in case of intrusion detection mechanism. In addition to this, the framework is able to detect U2R and R2L attacks more efficiently than previous findings, boosting up the detection process. In future, some more work can be made in order to detect U2R and R2L attacks more accurately which may tend to further enhance the system efficiency.

 

 

 

 

 

 

 

 

 

[1] KDDCUP-99 task description. https://kdd.ics.uci.edu/databases/kddcup99/task.html. [2] Deepthy K Denatious & Anita John, “Survey on Data Mining Techniques to Enhance Intrusion Detection”, International Conference on Computer Communication and Informatics (ICCCI -2012), Jan. 10 – 12, 2012, Coimbatore, INDIA [3] Srinivas Mukkamala, Andrew H. Sung, Ajith Abraham, Intrusion detection using an ensemble of intelligent paradigms, Elsevier, Journal of Network and Computer Applications 28 (2005) pp.-167–182. [4] Sandhya Peddabachigari, Ajith Abraham,Crina Grosan, Johnson Thomas, Modeling intrusion detection system using hybrid intelligent systems, Elsevier, Journal of Network and Computer Applications 30 (2007), pp.114- 132. [5] KddCup99 dataset, available at http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html, 1999. [6] Rung-Ching Chen, Kai-Fan Cheng and Chia-Fen Hsieh, “Using Rough Set And Support Vector Machine For Network Intrusion Detection”, International Journal of Network Security & Its Applications (IJNSA), Vol 1, No 1, April 2009 [7] Kohavi R, John GH (1997) Wrappers for feature subset selection. Artif Intell 97:273–324 [8] Wei-Hao Lin and Alexander Hauptmann, Metaclassification: Combining Multimodal Classifiers, Springer, Mining Multimedia and Complex Data, LNAI 2797 (2003) pp. 217–231. [9] Peddabachigiri S., A. Abraham., C. Grosan and J. Thomas, “Modeling of Intrusion Detection System Using Hybrid intelligent systems” , Journals of network computer application, 2007 [10] Mrutyunjaya Panda and Manas Ranjan Patra, “A Comparative Study Of Data Mining Algorithms For Network Intrusion Detection”, First International Conference on Emerging Trends in Engineering and Technology, pp 504-507, IEEE, 2008 [11] M.Govindarajan and Rlvl.Chandrasekaran, “Intrusion Detection Using k-Nearest Neighbor” pp 13-20, ICAC, IEEE, 2009 [12] Mohammadreza Ektefa, Sara Memar, Fatimah Sidi and Lilly Suriani Affendey, “Intrusion Detection Using Data Mining Techniques”, pp 200-203, IEEE, 2010FRNN(U, C, y) [13] Roshan Chitrakar and Huang Chuanhe, “Anomaly based Intrusion Detection using Hybrid Learning Approach of combining k-Medoids Clustering and Naïve Bayes Classification”, IEEE,2012 [14] David Ndumiyana, Richard Gotora and Hilton Chikwiriro, “Data Mining Techniques in Intrusion Detection: Tightening Network Security”, International Journal of Engineering Research & Technology (IJERT), Vol. 2 Issue 5, May – 2013 [15] Muhammad K. Asif, Talha A. Khan,Talha A. Taj, Umar Naeem and Sufyan Yakoob, “ Network Intrusion Detection and its Strategic Importance”, Business Engineering and Industrial Applications Colloquium(BEIAC), IEEE, 2013 [16] Kapil Wankhade, Sadia Patka and Ravindra Thools, “An Efficient Approach for Intrusion Detection Using Data Mining Methods”, IEEE 2013 [17] Vaishali B Kosamkar and Sangita S Chaudhari, “Data Mining Algorithms for Intrusion Detection System: An Overview”, International Conference in Recent Trends in Information Technology and Computer Science (ICRTITCS), 2012 [18] Iwan Syarif, Adam Pruge Bennett and Gary Wills, “Unsupervised clustering approach for network anomaly detection”, IEEE. [19] Wei-Hao Lin and Alexander Hauptmann, Metaclassification: Combining Multimodal Classifiers, Springer, Mining Multimedia and Complex Data, LNAI 2797 (2003) pp. 217–231. [20] Alexandra M. Carvalho, Arlindo L. Oliveira and Marie- France Sagot, Efficient learning of Bayesian network classifiers: An extension to the TAN classifier, Proceedings of Advances in Artificial Intelligence, Springer, Volume 4830, (2007), pp 16-25. [21] Ajit Singh, Tree-augmented naive bayes, Homework 2 Problem 7 of Probabilistic Graphical Models, Fall 2006.