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Intrusion Detection- A Comparative Analysis using Classification Algorithms
Abstract
Intrusion Detection System is gaining more popularity nowadays as everyone is keen on protecting their networks. It is used to identify various authentic and malicious activities in the system and network. A lot of research activities are taking place to protect the network from outsiders as well as insiders. Various soft computing techniques like Data Mining, Artificial Intelligence are used in Intrusion Detection System for identifying malicious activities. In this paper we have done a survey on four supervised machine learning algorithms: Decision Tree (J48), K-Nearest Neighbor(KNN), Naïve Bayes (NB) and Support Vector Machine(SVM).We have shown a comparative analysis of these algorithms based on Accuracy, True Positive Rate(TPR) and False Positive Rate(FPR). We have used NSL-KDD dataset for our experiment. Based on the experimental result, we have shown that the performance of Decision Tree (J48) and K-Nearest Neighbor are better than other two algorithms in terms of Accuracy, True Positive Rate (TPR) and False Positive Rat (FPR).
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References
Amanpreet Chauhan, Gaurav Mishra, Gulshan Kumar,"Survey on Data Mining Techniques in Intrusion Detection", International Journal of Scientific & Engineering Research Volume 2, Issue 7, July-2011 ISSN 2229-5518.
Yogendra Kumar Jain and Upendra,”An efficient Intrusion Detection Based on Decision Tree classifier using Feature Reduction”, International journal of Scientic Research Publication,Vol 2, issue 1,January 2012.
Vıctor H. Garcıa, Ra´ul Monroy, and Maricela Quintana,"Web Attack Detection Using ID3” Computer Science Department Tecnologico de Monterrey, Campus Estado de Mexico.
L Prema RAJESWARI, Kannan ARPUTHARAJ,” An Active Rule Approach for Network Intrusion Detectionwith Enhanced C4.5 Algorithm” I. J. Communications, Network and System Sciences, 2008, 4, 285-385
Srilatha Chebrolua, Ajith Abrahama,b, Johnson P. Thomasa, "Feature deduction and ensemble design of intrusion detection systems", www. elsevier.com/locate/cose
Nahla Ben Amor1, Salem Benferhat2 and Zied Elouedi,"Naive Bayesian Networks in Intrusion Detection Systems".
Reema Patel, Amit Thakkar, Amit Ganatra,"A Survey and Comparative Analysis of Data Mining Techniques for Network Intrusion detection System”, International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-1, March 2012.
Neethu B,” Classification of Intrusion Detection Dataset using machine learning Approaches”, nternational Journal of Electronics and Computer Science Engineering.
Deepika Dave Prof. Vineet Richhariya, "INTRUSION DETECTION WITH KNN CLASSIFICATION AND DS- THEORY",IRACST - International Journal of Computer Science and Information Technology & Security (IJCSITS), ISSN: 2249-9555.
Yihua Liao Leo,Rao Vemori ,” Classifier Using K Nearest for Intrusion Detection System”.
Ray-I Chang, Liang-Bin Lai , Wen-De Su*, Jen-Chieh Wang*, Jen-Shiang Kouh,” Intrusion Detection by Backpropagation Neural Networks with Sample-Query and Attribute-Query”, International Journal of Computational Intelligence Research. ISSN 0973-1873 Vol.3, No. 1 (2007), pp. 6-10
Adel Jahanbani 1, Manijeh Keshtgari 2, S Amirhassan Monadjemi3,” Intrusion Detection System Using New Synthetic Neural Networks”, Journal of Basic and Applied Scientific Research www.textroad.com
Tich Phuoc Tran, Longbing Cao,” Novel Intrusion Detection using Probabilistic Neural Network and Adaptive Boosting”, (IJCSIS) International Journal of Computer Science and Information Security,Vol. 6, No. 1, 2009.
Chandrika Palagiri,” Network-Based Intrusion Detection Using Neural Networks”, epartment of Computer Science Rensselaer Polytechnic Institute Troy, New York 12180-3590.
The NSL-KDD Data Set nsl.cs.unb.ca/NSL-KDD
kdd.ics.uci.edu/databases/kddcup99/kddcup99.html
Theodoros Lappas and Konstantinos Pelechrinis,"Data Mining Techniques for (Network) IntrusionDetection Systems",Department of Computer Science and Engineering UC Riverside, Riverside CA 92521
Kwak Ho Law,” IDS false alarm filtering using KNN classifier”, Proceeding WISA'04 Proceedings of the 5th international conference on Information Security Applications Pages 114-121Springer-Verlag Berlin, Heidelberg ©2005
Sandhya Peddabachigari, Ajith Abraham*, Johnson Thomas,"Intrusion Detection Systems Using Decision Trees and Support Vector Machines”.
Yacine Bouzida,” Intrusion Detection Systems Using Decision Trees and Support Vector Machines Neural networks vs. decision trees for intrusion detection”.
Sharma and Sanjay Kumar,” means clustering via Naïve Bayes Classification for anomaly based intrusion” IEEE-2012 Conference Advances in Engineering Science & Management.
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