The Intrusion Detection System helps people and organization to detect the attacks, hackers, their logging information and report these information to the owner of the computer system. The Intrusion Detection System not only identifies the attack on the computer system, it also determines problems with current security policies. The popular conventional security mechanisms are – authentication and firewall security. The authentication protects the computer integrity and security from unauthorized person but it cannot prevent authorized (legitimate) users from performing harmful operations on a computer system. On the other hand firewall only security from some internal attacks to the computer peripherals and information, it cannot provide complete security from outside attacks on the internet. The intrusion detection system is a powerful technology that provides security from both the inside as well as outside attacks. In the world of communication, we exchange our data with another users using internet. Also in the age of cloud computing our data is stored on the remote computer which can be accessed using Internet. Therefore, security of data is big concern for different users. We need not only to protect the data, which exchanged through internet but also to protect the stored data from different types of attacks. An Intrusion Detection System does all the above activities for us. Successful Intrusion Detection Systems protect computer systems from various types of computer system attacks. We can construct Intrusion Detection Systems on various platforms. One such platform is data mining.

Intrusion Detection System, Classifications of IDS

[1] Suthaharan, S. (2012). An iterative ellipsoid-based anomaly detection technique for intrusion detection systems. 2012 Proceedings of IEEE Southeastcon.
[2] Ng, J., Joshi, D., & Banik, S. M. (2015). Applying Data Mining Techniques to Intrusion Detection. 2015 12th International Conference on Information Technology - New Generations.
[3] Zhou, Z., Liu, L., & Han, G. (2015). Survival Continuity on Intrusion Detection System of Wireless Sensor Networks. 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC).
[4] Mehmood, T., & Rais, H. B. M. (2016). Machine learning algorithms in context of intrusion detection. 2016 3rd International Conference on Computer and Information Sciences (ICCOINS).
[5] Jaiswal, A., Manjunatha, A. S., Madhu, B. R., & Murthy, P. C. (2016). Predicting unlabeled traffic for intrusion detection using semi-supervised machine learning. 2016 International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT).
[6] Borkar, A., Donode, A., & Kumari, A. (2017). A survey on Intrusion Detection System (IDS) and Internal Intrusion Detection and protection system (IIDPS). 2017 International Conference on Inventive Computing and Informatics (ICICI).
[7] Samrin, R., & Vasumathi, D. (2017). Review on anomaly based network intrusion detection system. 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT).
[8] Zhang, Q., Qu, Y., & Deng, A. (2018). Network Intrusion Detection Using Kernel-based Fuzzy-rough Feature Selection. 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).
[9] Gupta, D., Singhal, S., Malik, S., & Singh, A. (2016). Network intrusion detection system using various data mining techniques. 2016 International Conference on Research Advances in Integrated Navigation Systems (RAINS).
[10] Wankhade, K., Patka, S., & Thool, R. (2013). An efficient approach for Intrusion Detection using data mining methods. 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI).
[11] Sultana, A., & Jabbar, M. A. (2016). Intelligent network intrusion detection system using data mining techniques. 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT).
[12] Sharma, B., & Gupta, H. (2014). A Design and Implementation of Intrusion Detection System by Using Data Mining. 2014 Fourth International Conference on Communication Systems and Network Technologies.
[13] El Moussaid, N., & Toumanari, A. (2014). Overview of intrusion detection using data-mining and the features selection. 2014 International Conference on Multimedia Computing and Systems (ICMCS).
[14] Deepa, V. K., & Geetha, J. R. R. (2013). Rapid development of applications in data mining. 2013 International Conference on Green High Performance Computing (ICGHPC).
[15] Bjerkestrand, T., Tsaptsinos, D., & Pfluegel, E. (2015). An evaluation of feature selection and reduction algorithms for network IDS data. 2015 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA).
[16] China Appala Naidu, R., & Avadhani, P. S. (2012). A comparison of data mining techniques for intrusion detection. 2012 IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT).
[17] Ariafar, E., & Kiani, R. (2017). Intrusion detection system using an optimized framework based on datamining techniques. 2017 IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI).
[18] Elekar, K. S. (2015). Combination of data mining techniques for intrusion detection system. 2015 International Conference on Computer, Communication and Control (IC4).
[19] Das, A., & Sathya, S. S. (2012). A fuzzy approach to feature reduction in KDD intrusion detection dataset. 2012 Third International Conference on Computing, Communication and Networking Technologies (ICCCNT’12).
[20] Prachi Tembhare1, Neeraj Shukla (2017)[20] An Integrated and Improved Scheme for Efficient Intrusion Detection in Cloud.
[21] P. Rutravigneshwaran (2017) A Study of Intrusion Detection System using Efficient Data Mining Techniques.