• Prof.Dr.G.Manoj Someswar
  • Venkata Reddy Medikonda


In this research paper, we give point by point depiction to the proposed systems and calculations utilized as a part of investigating behavioral differences. The general structure of BDA and usage of the analyzers are clarified too, to be joined by execution points of interest of different components in the proposed engineering.

This area is given to clarification of factual strategies and learning systems utilized by the analyzers, including observing of the difference estimations of the components utilizing exponentially weighted moving normal strategy, modifying those parameters that speak to reaction time difference of the servers, tuning measurable parameters, and performing incremental learning.


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Sankalp Singh, Michel Cukier, and William H. Sanders, Probabilistic validation of an intrusion-tolerant replication system, Proceedings of the Inter-national Conference on Dependable Systems and Networks (DSN ’03) (San Francisco, CA, USA), June 2003, pp. 615–624.

Ambareen Siraj, Rayford B. Vaughn, and Susan M. Bridges, Intrusion sensor data fusion in an intelligent intrusion detection system architecture, Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS’04) - Track 9 (Washington, DC, USA), IEEE Computer Society, 2004, p. 90279.3.(HICSS’04) - Track 9 (Washington, DC, USA), IEEE Computer Society, 2004, p. 90279.3.

S. R. Snapp, J. Brentano, G. V. Dias, T. L. Goan, L. T. Heberlein, C. L. Ho, K. N. Levitt, B. Mukherjee, S. E. Smaha, T. Grance, D. M. Teal, and D. Mansur, DIDS (Distributed Intrusion Detection System) - motivation, architecture and an early prototype, Proceedings of the 14th National Computer Security Conference (Washington, D.C.), October 1991, pp. 167–176.

Ming Tham, Dealing with measurement noise, fillpass.htm, accessed December 2005.

Eric Totel, Frédéric Majorczyk, and Ludovic Mé, COTS diversity based in-trusion detection and application to web servers, Proceedings of the Recent Advances in Intrusion Detection Symposium (RAID’05), September 2005, pp. 43–62.

Thomas Toth and Christopher Kruegel, Evaluating the impact of automated intrusion response mechanisms, Proceedings of 18th Annual Computer Security Applications Conference (Las Vegas, NV, USA), IEEE Computer Society, December 2002, pp. 301–310.

Jeffrey Undercoffer, Anupam Joshi, and John Pinkston, Modeling computer attacks: An ontology for intrusion detection, Proceedings of the Sixth Inter-national Symposium on Recent Advances in Intrusion Detection (RAID’03), Springer-Verlag, LNCS 2820, September 2003, pp. 113–135.

Feiyi Wang, Fengmin Gong, Chandramouli Sargor, Katerina Goseva-Popstojanova, Kishor Trivedi, and Frank Jou, SITAR: A Scalable Intrusion-Tolerant Architecture for Distributed Services, Proceedings of the 2001 IEEE

Workshop on Information Assurance and Security (West Point, NY, USA),June 2001, pp. 38–45.

R. Wang, F. Wang, , and G. Byrd, Design and implementation of acceptance monitor for building scalable intrusion tolerant system, Software Practice and Experience 33 (2003), no. 1, 1399–1417.

Daniel Weber, A taxonomy of computer intrusions, Master’s thesis, Massachusetts Institute Of Technology, Cambridge, MA, USA, June 1998.

How to Cite
Someswar, P., & Medikonda, V. R. (2017). ENGINEERING AND INVESTIGATING BEHAVIORAL CHANGES FOR PROPOSED SYSTEMS IN PERFORMING INCREMENTAL LEARNING PATTERNS. COMPUSOFT: An International Journal of Advanced Computer Technology, 6(8). Retrieved from