NDLI: Mining Concept Drifting Network Traffic in Cloud Computing Environments

Content Provider	IEEE Xplore Digital Library
Author	Mukkavilli, S.K. Shetty, S.
Copyright Year	2012
Abstract	Anomaly-based network Intrusion Detection Systems (IDS)model patterns of normal activity and detect novel network attacks. However, these systems depend on the availability of the systems normal traffic pattern profile. But the statistical fingerprint of the normal traffic pattern can change and shift over a period of time due to changes in operational or user activity at the networked site or even system updates. The changes in normal traffic patterns over time lead to concept drift. Some changes can be temporal, cyclical and can be short-lived or they can last for longer periods of time. Depending on a number of factors the speed at which the change in traffic patterns occurs can also be variable, ranging from near instantaneous to the change occurring over the span of numerous months. These changes in traffic patterns are a cause of concern for IDSs as they can lead to a significant increase in false positive rates, thereby reducing the overall system performance. In order to improve the reliability of the IDS, there is a need for an automated mechanism to detect valid traffic changes and avoid inappropriate ad hoc responses. ROC curves have historically been used to evaluate the accuracy of IDSs. ROC curves generated using fixed, time-invariant classification thresholds do not characterize the best accuracy that an IDS can achieve in presence of concept-drifting network traffic. In this paper, we present integrated supervised machine learning and control theoretic model (especially for clouds) for detecting concept drift in network traffic patterns. The model comprises of an online support vector machine based classifier (incremental anomaly based detection), a Kullback-Leiblerdivergence based relative entropy measurement scheme (quantifying concept drift) and feedback control engine (adapting ROC thresholding). In our proposed system, any intrusion activity will cause significant variations, thereby causing a large error, while a minor aberration in the variations(concept drift) will not be immediately reported as alert.
Starting Page	721
Ending Page	722
File Size	409578
Page Count	2
File Format	PDF
ISBN	9781467313957
DOI	10.1109/CCGrid.2012.142
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-13
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Traffic control Adaptation models Intrusion detection Accuracy Support vector machines Hidden Markov models Telecommunication traffic Support Vector Machine and Concept Drift Anomaly Based Intrusion Detection Systems
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
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7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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