NDLI: Flow-based Statistical Aggregation Schemes for Network Anomaly Detection

Content Provider	IEEE Xplore Digital Library
Author	Sui Song Li Ling Manikopoulo, C.N.
Copyright Year	2006
Description	Author affiliation: Dept. of Electr. Eng., New Jersey Inst. of Technol., Newark, NJ (Sui Song; Li Ling; Manikopoulo, C.N.)
Abstract	In this paper, we present novel flow-based statistical aggregation schemes (FSAS) for network anomaly detection. An IP flow is a unidirectional series of IP packets of a given protocol, traveling between a source and destination, within a certain period of time. Based on "flow" concept, we developed a flow-based aggregation technique that dramatically reduces the amount of monitoring data and handles high amounts of statistics and packet data. FSSAS sets up flow-based statistical feature vectors and reports to a neural network classifier. The neural classifier uses back-propagation networks to classify the score metric of each flow. FSAS can detect both bandwidth type DOS and protocol type DOS. Moreover, flow here could be any set of packets sharing certain common property as "flow key". FSAS configures flow flexibly to provide security from network level to application level (IP, TCP, UDP, HTTP, FTP...), and different aggregation schemes, such as server-based, client-based flow. This novel IDS has been evaluated by using DARPA 98 data and CONEX test-bed data. Results show the success in terms of different aggregation schemes for both datasets
Sponsorship	IEEE SYSTEMS, MAN AND CYBERNETICS SOC
Starting Page	786
Ending Page	791
File Size	526573
Page Count	6
File Format	PDF
ISBN	1424400651
DOI	10.1109/ICNSC.2006.1673246
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-04-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Telecommunication traffic Intrusion detection Neural networks Internet Traffic control Protocols Monitoring Statistics Data security Fluid flow measurement Network Intrusion Detection System Flow Aggregation Neural Network Classifier
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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