IJSER Home >> Journal >> IJSER
International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 11    
Website: http://www.ijser.org
scirp IJSER >> Volume 2, Issue 11, November 2011
Layer Based Intrusion Detection System for Network Security (LBIDS)
Full Text(PDF, 3000)  PP.  
B. Uppalaiah, N. Vamsi Krishna, R. Rajendher
Network Security; Intrusion Detection; Layered Approach.
In this paper we present a general framework for an Intrusion Dection System which we called as Layer Based Intrusion Detection System (LBIDS). We base our framework on the fact that any network needs to ensure the confidentiality, integrity and availability of data and/or services which can be compromised only sequentially one after the other, i.e. availability followed by authentication and authorization and finally leading to loss of confidentiality and integrity. Our framework examines different attributes at different layers to effectively identify any breach of security at every layer. This would have the advantage of reducing the computation and increasing the detection accuracy. This is attributed to the fact that oncesan anomaly is detected at a layer; it saves the computation required by subsequent layer(s) by simply blocking it at the point of identification. Detection accuracy can be increased as the features that are selected to be evaluated to make any decision at a particular layer are optimized to detect that particular attack category
[1] http://www.cerias.purdue.edu/research/aafid/.autonomo us agents for intrusion detection. Online article (Last assessed: July 12 2006)

[2] http://www.cse.sc.edu/research/isl/agentIDS.shtml, probabilistic agent based approach for intrusion detection. Online article (Last assessed: July 06 2006)

[3] http://kdd.ics.uci.edu//databases/kddcup99/kddcup99,html. KDD Cup 1999 Data (Last assessed: July 02 2006)

[4] http://www.cs.unm.edu/~immsec/systemcalls.htm. Computer Immune Systems: (Last assessed: July 02 2006)

[5] http://www.windowssecurity.com/articles/HidsvsNidsPart1.html. Online article (Last assessed: July 02 2006)

[6] Y.Zhong, Z.Zhu, and X.L. Qin. A clustering method based on data queries and its application database intrusion. In proceedings of the fourth International Conference on Machine Learning and Cybemetics, IEEE Press, vol. (4), 2005, pages 2096-2101.

[7] Y.Hu and B.Panda. A datamining approach for database intrusion detection. In Proceedings of the 2004 ACM symposium on Applied Computing, ACM press, pages 711-716.

[8] D. Denning.vAn intrusion-detection model. IEEE Transactions on Software Engineering, vol. (SE-13), no. (2), 1987, pages 222-232. [12] Y. Du, H. Wang, and Y. Pang. A hiddenmarkov models-based anomaly intrusion detection method. In fifth World Congress on Intelligent Control and Automation, 2004, (WCICA’04), IEEE Press, vol. (5), 2004, pages 4348-4351.

[9] K. Ghosh. Learning program behavior profiles fD.S. Coming and O.G. Staadt, ""Veloor intrusion detection. In Proceedings of the 1st USENIX Workshop on Intrusion Detection and Network Monitoring, 1999 pages 51-62.

[10] K. K. Gupta, B. Nath, K. Rao, and A. Kazi. Attacking confidentiality: An agent based approach. In Proceedings of IEEE International Conference on Intelligence and Security Informatics, Lecture Notes in Computer Science, Springer Verlag, vol. (3975), 2006, pages 285- 296.[2].

Untitled Page