Intrusion Detection System Research Paper

Intrusion Detection System Research Paper-35
Williamson, “Throttling viruses: Restricting propagation to defeat malicious mobile code,"" ACSAC Security Conference, 2002. Stolfo, “Anomalous Payload-Based Network Intrusion Detection,” Recent Advances in Intrusion Detection (RAID), 2004. Mahoney, “Network Traffic Anomaly Detection Based on Packet Bytes,” ACM Symposium on Applied Computing (SAC), 2003. Future work may extend the system to detect intrusions implanted with hacking tools and not through straight HTTP requests or intrusions embedded in non‐basic resources like multimedia files and others, track illegal web users with their prior web‐access sequences, implement minimum and maximum values for integer data, and automate the process of pre‐processing training data so that it is clean and free of intrusion for accurate detection results.

Williamson, “Throttling viruses: Restricting propagation to defeat malicious mobile code,"" ACSAC Security Conference, 2002. Stolfo, “Anomalous Payload-Based Network Intrusion Detection,” Recent Advances in Intrusion Detection (RAID), 2004. Mahoney, “Network Traffic Anomaly Detection Based on Packet Bytes,” ACM Symposium on Applied Computing (SAC), 2003. Future work may extend the system to detect intrusions implanted with hacking tools and not through straight HTTP requests or intrusions embedded in non‐basic resources like multimedia files and others, track illegal web users with their prior web‐access sequences, implement minimum and maximum values for integer data, and automate the process of pre‐processing training data so that it is clean and free of intrusion for accurate detection results.

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Association rule mining technique is employed for mining frequent parameter list and their sequential order to identify intrusions.

Experiments show that proposed system has higher detection rate for web intrusions than SNORT and mod security for such classes of web intrusions like cross‐site scripting, SQL‐Injection, session hijacking, cookie poison, denial of service, buffer overflow, and probes attacks. https://doi.org/10.1108/17440080810865648 Download as .

Denning, An Intrusion-Detection Model, IEEE Transactions on Software Engineering, vol. of Computer Science and Engineering, IIT Khargpur 2008 Guy Bruneau – GSEC Version 1.2f,” The History and Evolution of Intrusion Detection”, SANS Institute 2001.

Dinakara K, “Anomaly Based Network Intrusion Detection System”, Thesis Report, Dept. Dickerson, “Fuzzy network profiling for intrusion detection,” In Proceedings of the 19th International Conference of the North American Fuzzy Information Processing Society (NAFIPS), 13-15 July 2000, pp. Debar H, Becker M, and Siboni D, “A Neural Network Component for an Intrusion Detection System”, IEEE Computer Society Symposium on Research in Security and Privacy, Los Alamitos Oakland, CA, pp. DK Bhattacharyya and JK Kalita, 2014, “Network Anomaly Detection: A Machine Learning Perspective”, CRC Press, Taylor & Francis Group, International Standard Book Number-13: 978-1-4665-8209-5 Bhuyan, M.

Mark Crosbie, Gene Spafford, Defending a Computer System using Autonomous Agents, Technical report No.

AINT misbehaving – A taxonomy of anti-intrusion techniques. of 18th NIST-NCSC National Information Systems Security Conference, pages 163–172, 1995. Ilgun, Koral, USTAT:a real time IDS for Unix, Proceedings of the 1993 IEEE Computer Society Symposium on research insecurity and privacy, 1993. Valdes, Next-generation intrusion detection expert system (NIDES), Technical report, SRI-CSL-95-07, SRI International, Computer Science Lab, May 1995." Paxson, Vern, Bro: A system for detecting network intruders in real-time, Computer Network, v 31, n 23, Dec 1999. S, Jajodia S, Modelling requests among cooperating IDSs, Computer Communications, v 23, n 17, Nov, 2000." J.

Sensor Web IDS has three main components: the network sensor for extracting parameters from real‐time network traffic, the log digger for extracting parameters from web log files and the audit engine for analyzing all web request parameters for intrusion detection.

To combat web intrusions like buffer‐over‐flow attack, Sensor Web IDS utilizes an algorithm based on standard deviation ( of the mean, to calculate the possible maximum value length of input parameters.

R., Mc-Clung, D., Weber, D., Webster, S., E., Wyschogrod, D., Cunningham, R.

Zerkle, Gr IDS – A Graph-Based Intrusion Detection System for Large Networks, The 19th National Information Systems Security Conference, Baltimore, MD., October 1996.

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