Abstract

Use of internet is increasing to great extent and with it abnormal and malicious activities. Solving problem of these attacks is becoming a prime need of network services. Till date many techniques and algorithms are developed. All these can be summed to intrusion detection systems and firewall. In this paper we present the classification of these intrusion detection systems on the basis of their properties. So it will help in understanding different IDS and their properties accordingly. With different types IDS classification it also enlists pros and cons of systems.

References

• Marion Bogdanov ,"An approach to developing an information assurance environment"
• Sanjay Kumar Sharma, Pankaj Pande, Susheel Kumar Tiwari and Mahendra Singh Sisodia ,"An Improved Network Intrusion Detection technique based on k-means clustering via naïve Byes Classification" IEEE-International Conference On Advances In Engineering, Science And Management (ICAESM -2012) March 30, 31, 2012
• Thanvarat Komviriyavut, Phurivit Sangkatsanee, Naruemon Wattanapongsakorn, "Network intrusion detection and classification with decision tree and rule based approach" IEEE, 2009
• Deris Stiawan, Ala' Yaseen Ibrahim Shakhatreh, Mohd. Yazid Idris, Kamarulnizam Abu Bakar, Abdul Hanan Abdullah, "Intrusion prevention system: a survey" Journal of Theoretical and Applied Information Technology 15 June 2012. Vol. 40 No. 1
• N. Wattanapongsakorn, S. Srakaew, E. Wonghirunsombat, C. Sribavonmongkol, T. Junhom, P. Jongsubsook, C. Charnsripinyo, "A Practical Network based Intrusion Detection and Prevention System" 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications
• K. B. Chandradeep, "A scheme for the design and implementation of a distributed ids" 2009 First International Conference on Networks & Communications
• Kjetil Haslum, Ajith Abraham and Svein Knapskog, "Fuzzy online risk assessment for distributed intrusion prediction and prevention systems" Tenth International Conference on Computer Modeling and Simulation
• Hakan Albag, "Network & agent based intrusion detection systems"
• Vinod Kumar, Dr. Om Prakash Sangwan, "Signature based intrusion detection system using Snort" International Journal of Computer Applications & Information Technology Vol. I, Issue III, November 2012 (ISSN: 2278-7720)
• V. Jyothsna, V. V. Rama Prasad, K. Munivara Prasad, "A review of anomaly based intrusion detection systems" International Journal of Computer Applications (0975 – 8887) Volume 28– No. 7, August 2011
• Maithili Arjunwadkar, R. V. Kulkarni, "The rule based intrusion detection and prevention model for biometric system" VOL. 1, NO. 2, Oct 2010 Journal of Emerging Trends in Computing and Information Sciences
• Todd Vollmer, Jim Alves-Foss, Milos Manic, "Autonomous rule creation for intrusion detection" 2011 IEEE Symposium on Computational Intelligence in Cyber Security
• Matti Manninen, "Using Artificial Intelligence in intrusion detection systems"
• Shaik Akbar, Dr. K. Nageswara Rao, Dr. J. A. Chandulal. "Implementing rule based genetic algorithm as a solution for intrusion detection system" IJCSNS International Journal of Computer Science and Network Security, VOL. 11 No. 8, August 2011
• Zorana Bankovic, Dus?an Stepanovic, Slobodan Bojanic, Octavio Nieto-Taladriz, "Improving network security using genetic algorithm approach" Computers and Electrical Engineering 33 (2007) 438–451
• Borgersen Gustav, Karlsson Linus, "Supervised learning in artificial neural networks"
• Peter Dayan, "Unsupervised Learning" Appeared in Wilson, RA & Keil, F, editors. The MIT Encyclopedia of the Cognitive Sciences
• Phurivit Sangkatsanee, Naruemon Wattanapongsakorn, and Chalermpol Charnsripinyo, "Real-time intrusion detection and classification"
• Richard Lippmann, Joshua W. Haines, David J. Fried, Jonathan Korba, Kumar Das, "The 1999 DARPA offline intrusion detection evaluation"