In this paper, we have been explored the brief review about the intrusion detection system. This review emphasizes about how to automatically and systematically build adaptable and extensible advanced intrusion detection system using data mining techniques and how to provide in-built prevention policies in the detection system so that it will reduce network administrator's system re-configuration efforts and application of sentiment analysis to enhance its performance. Intrusion detection and prevention is really widely researched filed and still there is a scope for its advancements. This review gives the requirement of advancement in current intrusion detection systems based on data mining technique in its introduction section. In related work, it focuses on the growth and research contributions made in the field of security with intrusion detection and prevention. In the section of objectives, it concludes the current research requirements and use of possible techniques to step forward in intrusion detection and prevention. Finally, the possible applications of the proposed research work are highlighted to make its sense in society and conclusion provides the actual research direction based on the review.

1. Adeeb Alhomoud, Rashid Munir,Jules Pagna Disso,Irfan Awan,A. Al-Dhelaan (2011), "Performance Evaluation Study of Intrusion Detection Systems", Procedia Computer Science ,pp. 173–180.
2. A. Lazarevic, L. Ertoz, V. Kumar, A. Ozgur, and J. Srivastava(2003), "A Comparative Study of Anomaly Detection Schemes in Network Intrusion Detection", Proc. Third SIAM Conf. Data Mining,pp. 1-12.
3. Aurobindo Sundaram (1996), "An Introduction to Intrusion Detection", pp. 1-10.
4. B. Casewell and J. Beale (2004), SNORT 2. 1, Intrusion Detection, Syngress Pub, Second Edition.
5. Bing Li (2010), "Sentiment Analysis: A Multi-Faceted Problem", IEEE Intelligent Systems, pp. 1-5.
6. CSI and FBI (2010), "CSI & FBI Report 2010", pp. 1-2.
7. D. J. Burroughs, L. F. Wilson, and G. V. Cybenko (2002), "Analysis of Distributed Intrusion Detection Systems Using Bayesian Methods Performance", Proc. IEEE Int'l Computing and Comm. Conf. , pp. 329-334.
8. D. J. Ragsdale, C. A. Carver, J. Humphries, and U. Pooch (2000), "Adaptation Techniques for Intrusion Detection and Response Systems," Proc. IEEE Int'l Conf. Systems, Man, and Cybernetics, pp. 2344-2349.
9. E. Eskin, A. Arnold, M. Prerau, L. Portnoy, and S. Stolfo (2002), "A Geometric Framework for Unsupervised Anomaly Detection: Detecting Intrusions in Unlabeled Data", Applications of Data Mining in Computer Security, Kluwer Academic Publishers, pp. 1-20.
10. F. Cuppens and A. Miege (2002), "Alert Correlation in a Cooperative Intrusion Detection Framework", Proc. 2002 IEEE Symp. Security and Privacy, pp. 187-200.
11. Fengmin Gong (2003), "Next Generation Intrusion Detection Systems (IDS)", McAfee Network Protection Solutions, pp. 1-5.
12. Hesham Altwaijry, Saeed Algarny (2012), "Bayesian based intrusion detection system", Journal of King Saud University – Computer and Information Sciences, pp. 1–6.
13. Hesham Altwaijry, Saeed Algarny (2011), "Multi-Layer Bayesian Based Intrusion Detection System", Proceedings of the World Congress on Engineering and Computer Science 2011 Vol IIWCECS 2011 ISBN: 978-988-19251-7-6, pp. 1-5.
14. K. Hwang, Y. Chen, and H. Liu (2005), "Defending Distributed Computing Systems from Malicious Intrusions and Network Anomalies", Proc. IEEE Workshop Security in Systems and Networks (SSN '05) held with the IEEE Int'l Parallel & Distributed Processing Symp, pp. 1-8.
15. K. Hwang, Y. Kwok, S. Song, M. Cai, Y. Chen, and Y. Chen(2006), "DHT-Based Security Infrastructure for Trusted Internet and Grid Computing", Int'l J. Critical Infrastructures, vol. 2, no. 4, pp. 412- 433.
16. K. S. Killourhy and R. A. Maxion (2002), "Undermining an Anomaly-Based Intrusion Detection System Using Common Exploits", Proc. Int'l Symp. Recent Advances in Intrusion Detection (RAID '02), pp. 54-73.
17. L. Ertoz, E. Eilertson, A. Lazarevic, P. Tan, J. Srivastava, V. Kumar, and P. Dokas(2004), "The MINDS—Minnesota Intrusion Detection System", Chapter 3:Next Generation Data Mining, MIT Press,pp. 1-21.
18. M. Cai, K. Hwang, J. Pan, and C. Papadopoulos (2007), "WormShield: Fast Worm Signature Generation Using Distributed Fingerprint Aggregation", IEEE Trans. Dependable and Secure Computing, pp. 1-35.
19. M. V. Mahoney and P. K. Chan (2003), "An Analysis of the 1999 DARPA/ Lincoln Lab Evaluation Data for Network Anomaly Detection," Proc. Int'l Symp. Recent Advances in Intrusion Detection, pp. 220-237.
20. Muamer N. Mohammad, Norrozila Sulaiman, Osama Abdulkarim Muhsin (2011), "A Novel Intrusion Detection System by using Intelligent Data Mining in Weka Environment", Science Direct, Procedia Computer Science , pp. 1237–1242.
21. P. Ning, S. Jajodia, and X. S. Wang (2001), "Abstraction-Based Intrusion Detection in Distributed Environments," ACM Trans. Information and System Security, vol. 4, no. 4, pp. 407-452.
22. Rezk, H. Ali, M. El-Mikkawy and S. Barakat (2011), "Minimize the false positive rate in a database intrusion detection system", International Journal of Computer Science & Information Technology (IJCSIT) Vol 3, No 5, pp. 29-38.
23. R. P. Lippmann and J. Haines (2000), "Analysis and Results of the 1999 DARPA Off-Line Intrusion Detection Evaluation", Proc. Third Int'l Workshop Recent Advances in Intrusion Detection (RAID '00), H. Debar, L. Me, and S. F. Wu, eds. , pp. 162-182.
24. S. Noel, D. Wijesekera, and C. Youman (2002), "Modern Intrusion Detection, Data Mining, and Degrees of Attack Guilt", Applications of Data Mining in Computer Security, D. Barbara` and S. Jajodia, eds. , Kluwer Academic Publishers, pp. 1-29.
25. S. Sathya Bama, et al. (2011), "Network Intrusion Detection using Clustering: A Data Mining Approach", International Journal of Computer Applications (0975 – 8887) Volume 30– No. 4, pp. 14-17.
26. V. Paxson (1998), "Bro: A System for Detecting Network Intrusions in Real Time," Proc. Seventh USENIX Security Symp. , pp. 1-18
27. W. Fan, M. Miller, S. Stolfo, W. Lee, and P. Chan (2001), "Using Artificial Anomalies to Detect Unknown and Known Network Intrusions", Proc. First IEEE Int'l Conf. Data Mining, pp. 123-130.
28. W. Lee, S. J. Stolfo, and K. Mok(2000),"Adaptive Intrusion Detection: A Data Mining Approach", Artificial Intelligence Rev. , vol. 14, no. 6, pp. 533-567, Kluwer Academic Publishers, pp. 1-40.
29. W. Lee and S. Stolfo (2000), "A Framework for Constructing Features and Models for Intrusion Detection Systems", ACM Trans. Information and System Security (TISSec), pp. 227-261.
30. Xiangyang Zheng, Qian He (2011), "Research on Distributed Intrusion Detection System Model", Energy Procedia, pp. 1480-1485.
31. Yang Lan (2011), "Design and Implementation of Intrusion Detection System Based on Data Mining", Energy Procedia 13, pp. 5645-5651.
32. Yanjie Zhao (2011), "Research of Network Intrusion Detection System Based on Data Mining", Energy Procedia, pp. 1126 – 1132.
33. Yuanqin Wu, Liang Shi, Beizhan Wang, Panhong Wang, Yangbin Liu (2011), "Research on Intrusion Detection Based on Sequential Pattern Mining Algorithms", Science Direct Energy Procedia , pp. 505 – 511.

Anomaly Attack Intrusion Misuse Signature Prevention Policy