Database Intrusion Prevention Cum Detection System with Appropriate Response

Web based applications such as search engines, web mail, shopping carts and portal system are extensively used nowadays. Such technological advancement had not only led enterprises to develop it for being proficient but at the present they are heavily dependent on it. The attackers knowing increase in availability of such services are trying to search weaknesses in the system to gain access and perform malicious activities. Most intrusions are committed from within the organization by employees. That’s why defending database against both internal and external attacks is becoming more vital. Database Intrusion Detection System can be deployed to detect potential violations in database security and to minimize the risk of attacks. In this paper, we have explored about the various vulnerabilities to database, the different types of attacks and the existing intrusion detection techniques for database system. The architecture of Database Intrusion Prevention cum Detection System with appropriate Response has also been proposed. The proposed architecture uses Genetic Algorithm for intrusion detection.

[1]  Elisa Bertino,et al.  Mechanisms for database intrusion detection and response , 2008, IDAR '08.

[2]  Elisa Bertino,et al.  Intrusion detection in RBAC-administered databases , 2005, 21st Annual Computer Security Applications Conference (ACSAC'05).

[3]  Architectures for intrusion tolerant database systems , 2002, 18th Annual Computer Security Applications Conference, 2002. Proceedings..

[4]  Yong Peng,et al.  A Practical Database Intrusion Detection System Framework , 2009, 2009 Ninth IEEE International Conference on Computer and Information Technology.

[5]  B. Borowik,et al.  Modern Approaches to the Database Protection , 2007, 2007 4th IEEE Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications.

[6]  Dorothy E. Denning,et al.  An Intrusion-Detection Model , 1987, IEEE Transactions on Software Engineering.

[7]  Michael Schatz,et al.  Learning Program Behavior Profiles for Intrusion Detection , 1999, Workshop on Intrusion Detection and Network Monitoring.

[8]  Victor C. S. Lee,et al.  Intrusion detection in real-time database systems via time signatures , 2000, Proceedings Sixth IEEE Real-Time Technology and Applications Symposium. RTAS 2000.

[9]  Shukor Abd Razak,et al.  System architecture for SQL injection and insider misuse detection system for DBMS , 2008, 2008 International Symposium on Information Technology.

[10]  Ding-Zhu Du,et al.  Wireless Network Security , 2009, EURASIP J. Wirel. Commun. Netw..

[11]  Wu Gongxing,et al.  Design of a New Intrusion Detection System Based on Database , 2009, 2009 International Conference on Signal Processing Systems.

[12]  Frank S. Rietta Application layer intrusion detection for SQL injection , 2006, ACM-SE 44.

[13]  Sin Yeung Lee,et al.  Learning Fingerprints for a Database Intrusion Detection System , 2002, ESORICS.

[14]  Eugene H. Spafford,et al.  An Application of Pattern Matching in Intrusion Detection , 1994 .

[15]  Yi Hu,et al.  Identification of malicious transactions in database systems , 2003, Seventh International Database Engineering and Applications Symposium, 2003. Proceedings..

[16]  Gang Chen,et al.  An Immunity-Based Intrusion Detection Solution for Database Systems , 2005, WAIM.

[17]  Michael Gertz,et al.  DEMIDS: A Misuse Detection System for Database Systems , 2000, IICIS.

[18]  Stefan Axelsson Research in Intrusion-Detection Systems: A Survey , 1998 .

[19]  Hung Q. Ngo,et al.  A Data-Centric Approach to Insider Attack Detection in Database Systems , 2010, RAID.