In recent years, network based services and network based attacks have grown significantly. The network based attacks can also be considered as some kind of intrusion. Intrusion can be defined as "any set of actions that attempt to compromise the integrity, confidentiality or availability of a resource". For controlling intrusion, intrusion detection systems are employed. The three important characteristics of intrusion detection systems are accuracy, extensibility and adaptability. The attacks generally change their types; so we need to update the detection rules to notice new attacks. Several techniques such as data mining, statistics, and genetic algorithm have been used for intrusion detection. Most recently, the data mining techniques have been used to mine the normal pattern from the audit data. This paper presents the survey on data mining Algorithms applied on intrusion detection systems for the effective identification of both known and unknown patterns of attacks, thereby helping the users to develop secure information systems.

1. Gudadhe,M. , Prasad,p. , and Wankhade,k. 2010. A New Data Mining Based Network Intrusion detection Model".
2. Duanyang, Zhao. , Qingxiang, Xu. , and Zhilin Feng. 2010 Analysis and Design for Intrusion Detection System Based on Data Mining .
3. Xiaohui, Bao. , Tianqi, Xu. , and Hui, Hou. 2009. Network Intrusion Detection Based on Support Vector Machine
4. Ektefa, M. , Memar, S. , Sidi, F. , and Affendey, L. S. 2010 Intrusion Detection Using Data Mining Techniques.
5. Zhi, Song. Pan. , Song-Can, Chen. , Gen-Bao, Hu. , Dao, Qiang. Zhang. 2003. Hybrid Neural Network And C4. 5 For Misuse Detection.
6. Jiong, Zhang. , Zulkernine, M. , Haque, 2008 Random-Forests-Based Network Intrusion Detection Systems.
7. Sharma, Sanjay. Kumar. , Pandey, Pankaj. Tiwari. , Susheel, Kumar and Sisodia, Mahendra. Singh. 2012. An Improved Network Intrusion Detection Technique based on k-Means Clustering via NaIve Bayes Classification.
8. Bin, Zeng. , Lu, Yao. , ZhiChen, Chen. 2010. A Network Intrusion Detection System with the Snooping Agent.
9. Fang, Yu. Ke. , Fu, Yan. , Zhou, Jun. Lin. 2010 Research of Outlier Mining Based Adaptive Intrusion Detection Techniques.
10. Subramanian,P. R. , Robinson, J. W. 2012. Alert Over the Attacks of Data Packet and Detect the Intruders.
11. Manish, Jain. , Vineet, Richariya. 2012. An Improved Techniques Based on Naive Bayesian for Attack Detection.
12. Amuthan. Prabakar. Muniyandia. , R, Rajeswarib. , R, Rajaramc. 2011. Network Anomaly Detection by Cascading K-Means Clustering and C4. 5 Decision Tree algorithm.
13. Gerhard, Munz. , Sa. Li. , and Georg Carle. Traffic Anomaly Detection Using K-Means Clustering.
14. Mrutyunjaya, Panda. and Manas, Ranjan. Patra. 2007. Network Intrusion Detection Using Naive Bayes.
15. Weiming, Hu. , Wei, Hu and May bank, S. 2008. AdaBoost-Based Algorithm for Network Intrusion Detection.
16. Shekhar,R. ,Gaddam, Vir. V. Phoha, Kiran, S. Balagani. 2007. K-Means+ID3: A Novel Method for Supervised Anomaly Detection by Cascading K-Means Clustering and ID3 Decision Tree Learning Methods.
17. Om, H. Kundu. 2012. A Hybrid System for Reducing the False Alarm Rate of Anomaly Intrusion Detection System.
18. Denatious, D. K. , and John, A. 2012. Survey on Data Mining Techniques to Enhance Intrusion Detection.
19. LI, Min. , An, Yang. Application of Data Mining Techniques in Intrusion Detection.

Intrusion Detection Intrusion Detection System Data Mining Data Mining Technique