A new data mining based network Intrusion Detection model

Nowadays, as information systems are more open to the Internet, the importance of secure networks is tremendously increased. New intelligent Intrusion Detection Systems (IDSs) which are based on sophisticated algorithms rather than current signature-base detections are in demand. There is often the need to update an installed Intrusion Detection System (IDS) due to new attack methods or upgraded computing environments. Since many current Intrusion Detection Systems are constructed by manual encoding of expert knowledge, changes to them are expensive and slow. In data mining-based intrusion detection system, we should make use of particular domain knowledge in relation to intrusion detection in order to efficiently extract relative rules from large amounts of records. This paper proposes new ensemble boosted decision tree approach for intrusion detection system. Experimental results shows better results for detecting intrusions as compared to others existing methods.

[1]  Quanyuan Wu,et al.  Mining Concept-Drifting and Noisy Data Streams Using Ensemble Classifiers , 2009, 2009 International Conference on Artificial Intelligence and Computational Intelligence.

[2]  Hans-Peter Kriegel,et al.  LOF: identifying density-based local outliers , 2000, SIGMOD '00.

[3]  Philip S. Yu,et al.  Outlier detection for high dimensional data , 2001, SIGMOD '01.

[4]  Raymond T. Ng,et al.  Algorithms for Mining Distance-Based Outliers in Large Datasets , 1998, VLDB.

[5]  R.K. Cunningham,et al.  Evaluating intrusion detection systems: the 1998 DARPA off-line intrusion detection evaluation , 2000, Proceedings DARPA Information Survivability Conference and Exposition. DISCEX'00.

[6]  W. R. Buckland,et al.  Outliers in Statistical Data , 1979 .

[7]  Bane Raman Raghunath,et al.  Network Intrusion Detection System (NIDS) , 2008, 2008 First International Conference on Emerging Trends in Engineering and Technology.

[8]  Rajeev Rastogi,et al.  Efficient algorithms for mining outliers from large data sets , 2000, SIGMOD 2000.

[9]  Latifur Khan,et al.  Classifying Evolving Data Streams for Intrusion Detection , 2009 .

[10]  Salvatore J. Stolfo,et al.  Data Mining Approaches for Intrusion Detection , 1998, USENIX Security Symposium.

[11]  Zhi-Xin Yu,et al.  A novel adaptive intrusion detection system based on data mining , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[12]  Sridhar Ramaswamy,et al.  Efficient algorithms for mining outliers from large data sets , 2000, SIGMOD '00.

[13]  Dorothy E. Denning,et al.  An Intrusion-Detection Model , 1987, IEEE Transactions on Software Engineering.

[14]  Philip S. Yu,et al.  Mining concept-drifting data streams using ensemble classifiers , 2003, KDD '03.

[15]  Manas Ranjan Patra,et al.  Ensembling Rule Based Classifiers for Detecting Network Intrusions , 2009, 2009 International Conference on Advances in Recent Technologies in Communication and Computing.

[16]  Xuan Dau Hoang,et al.  Data Mining Methods for Network Intrusion Detection , 2004 .

[17]  Plamen P. Angelov,et al.  Evolving Fuzzy-Rule-Based Classifiers From Data Streams , 2008, IEEE Transactions on Fuzzy Systems.

[18]  Salvatore J. Stolfo,et al.  A data mining framework for building intrusion detection models , 1999, Proceedings of the 1999 IEEE Symposium on Security and Privacy (Cat. No.99CB36344).

[19]  Harold S. Javitz,et al.  The NIDES Statistical Component Description and Justification , 1994 .