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An Investigation of Intrusion Detection in UDP Data Streams

R. Sridevi, Dr. Rajan Chattamvelli

Abstract


Securing the local network is a crucial task for any system administrator, as the activities in a network vary widely from simple data searching to online commercial transactions in many organizations. Intrusion detection systems (IDS) are extremely useful in this task. Detecting intrusions and identifying various methods or types of intrusions play an important role for predicting an intrusion and securing the network in the long run. Data mining techniques are being increasingly used to study the data streams with good results in IDS. In this paper we propose to extract unique signatures from UDP data streams, and predict intrusion using data mining techniques. We have used the KDD cup 1999 dataset that contains a wide variety of intrusion attacks simulated in a military environment.

Keywords


Decision Trees, KDD Cup Dataset, Random Tree, Supervised Learning Model, Naïve Bayes.

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References


A.Sundaram, “An Introduction to intrusion detection”, ACM international conference.

E. Amoroso, Wykrywanie intruzów, Wydawnictwo RM, Warszawa 1999 (in Polish), Intrusion Detection Systems (IDS) Part I - (network intrusions; attack symptoms; IDS tasks; and IDS architecture) Published: Apr 07, 2003

Eugene H Spafford, The Internet Worm Program: An Analysis., In ACM Computer Communication Review, Jan 1989, 19(1), pages 17-57.

Christine Dartigue, Hyun Ik Jang, and Wenjun Zeng, “A New Data-Mining Based Approach for Network Intrusion Detection”, IEEE 2009, Proc. of the seventh annual communication Networks and Services Research conference.

S.Kumar, “Classification and detection of computer intrusions”, Ph.D thesis, Purdue Univ., West Lafayette, IN 1995.

Evgeniya Nikolova, Veselina Jecheva, “Anomaly Based Intrusion Detection Using Data Mining and String Metrics”, IEEE 2009 Proc. of the International conference on communications and Mobile computing.

Hui Wang , Guoping Zhang, Huiguo chen and Xueshu Jiang, “Mining Association Rules for Intrusion Detection”, IEEE 2009 International conference on Frontier of Computer Science and Technology.

P.Srinivasalu, et al, “Classifying the Network Intrusion Attacks using Data Mining Classification Methods and their Performance Comparison”, 2009, Proc of International Journal of Computer Science and Network Security, Vol.9 No.6.

Mohammadreza Ektefa, Sara Memar, Fatimah Sidi, Lilly Suriani Affendey, “Intrusion Detection Using Data Mining Techniques”, 2010 IEEE computer society.

F Angiulli, T Catarci, P Ciaccia, G Ianni, S Kimani, S Lodi, M Patella, G Santucci & C Sartori, “An integrated data mining and data presentation tool” Mrutyunjaya Panda and manas Ranjan patra,

“Network Intrusion Detection Using Naïve Bayes”, 2007, Proc of IJCSNS International Journal of Computer Science and Network Security, Vol 7, No 12.

Joong-Hee Lee, et al. “Effective Value of Decision Tree with KDD 99 Intrusion Detection Datasets for Intrusion Detection System”, Feb. 17-20, 2008 ICACT 2008.

Lior Rokach & Oded Maimon “Data Mining with Decision Trees”, Series in Machine perception and artificial Intelligence vol.69, World Scientific, Singapore.

Y. Freund & R. Schapire, “Machine Learning”, Proceedings of the Thirteenth International conference, 148–156.

Ben Kröse & Patrick van der Smagt, “An introduction to Neural Networks”

Christos Stergiou and Dimitrios Siganos, “Neural Networks”

H. Güneş Kayacık, A. Nur Zincir-Heywood, Malcolm I. Heywood, “Selecting Features for Intrusion Detection: A Feature Relevance Analysis on KDD 99 Intrusion Detection Datasets”.

Rajan Chattamvelli, “Data Mining Methods”, Narosa publishing house, Daryaganj, New Delhi-110 002


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