Abstract

Over the past several years, the Internet environment has become more complex and un-trusted. Enterprise networked systems are inevitably exposed to the increasing threats posed by hackers as well as malicious users internal to a network. Intrusion Detection System (IDS) technology is one of the important tools used now-a-days, to detect such threats, which is a predictable element of the computer network system. Various IDS techniques has been proposed, which identifies and alarms for such threats or attacks. Data mining provides a wide range of techniques to classify these attacks. Today's IDS faces a number of key challenging issues. The challenges like detect malicious activities of the large amount of network traffic. The main challenging if some attacks were sneaking as normal connection. [1] This paper provides a proposed system which performs well when compare to other IDS also introduce a comparative study of strong and weak points with other system on the attack detection rate of these existing classification techniques.

References

• Sneha Kumari1, Maneesh Shrivastava, "A Study Paper on IDS Attack Classification Using Various Data Mining Techniques": International Journal of Advanced Computer Research, Vol. 2 No. 3, Sep. , 2012.
• Naelah Okasha, Sherif M. Badr, Prof. A. Hegazy, "Towards Ontology-Based adaptive multilevel Model For Intrusion Detection and Prevention System (AMIDPS), ECS, Vol. 34, No. 5, Sep. , 2010. http://net2. shams. edu. eg/ecs/
• Heba Ezzat Ibrahim, Sherif M. Badr , M. Shaheen, "Adaptive Layered Approach using Machine Learning Techniques with Gain ratio for Intrusion Detection systems", IJCA, Vol. 56, No. 7, Oct. 2012. http://www. ijcaonline. org/archives/volume56/number7/8901-2928
• Heba Ezzat Ibrahim, Sherif M. Badr , M. Shaheen, "Phases vs. Levels using Decision Trees for Intrusion Detection Systems", IJCSIS, Vol. 10, No. 8, Aug 2012. http://sites. google. com/site/ijcsis/
• Sherif M. Badr, "Implementation of Intelligent Multi-Layer Intrusion Detection Systems (IMLIDS)", IJCA, Jan. 2013. http://www. ijcaonline. org/archives/volume61/number4/9918-4526
• Sherif M. Badr, "Adaptive Layered Approach using C5. 0 Decision Tree for Intrusion Detection Systems (ALIDS), IJCA, Mar. 2013. http://www. ijcaonline. org/archives/volume66/number22/11247-5956
• Kapil Kumar Gupta, Baikunth Nath, and Ramamohanarao Kotagiri, "Layered Approach Using Conditional Random Fields for Intrusion Detection", IEEE Transactions on dependable and secure Computing, vol. 5, no. 4, October-December 2008.
• NSL-KDD data set for network-based intrusion detection systems, Available on: http://nsl. cs. unb. ca/NSL-KDD/, March 2009.
• Asmaa Shaker Ashoor, Prof. Sharad Gore, "Importance of Intrusion Detection System (IDS)", International Journal of Scientific & Engineering Research (IJSER), Volume 2, Issue 1, January-2011.
• A. Kumaravel, M. Niraisha, "Comparison of two Multi-Classification Approaches for Detecting Network Attacks", IJAIR Vol. 2, Issue 5, may 2013.
• A. Kumaravel, M. Niraisha, "Multi-Classification Approach for Detecting Network Attacks,"International Conference on Information and communication Technologies (ICT 2013), ICT545,4, Apr. 2013.