NDLI: Detecting Malware Outbreaks Using a Statistical Model of Blackhole Traffic

Content Provider	IEEE Xplore Digital Library
Author	Soltani, S. Ali Khayam, S. Radha, H.
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Michigan State Univ., East Lansing, MI (Soltani, S.)
Abstract	Internet blackholes have emerged as very effective tools for monitoring changes in the Internet's traffic behavior. Prior studies have shown that traffic observed at a blackhole contains valuable information about emerging malware. While blackhole traffic has been effectively used for attack forensics, a systematic method of leveraging this traffic for online Internet- scale anomaly detection is not available. In this paper, we propose a novel technique to detect malware outbreaks using deviations in a robust statistical model of a blackhole's traffic. First, we introduce a novel and accurate Piecewise Poisson process Model (PPM) of traffic observed at an Internet Motion Sensor (IMS) blackhole which provides a statistical quantification of the intensity or rate of incoming traffic at a blackhole, which can in turn be used to detect malware outbreaks. After establishing the accuracy of the proposed PPM model, we develop a regression model that can characterize variations in the PPM's traffic rates. Once an accurate model of traffic rates is in place, malware outbreaks can be detected using deviations from the model's likely statistical patterns. After removing simple deterministic patterns, we observe that a blackhole's traffic rate residuals have a skewed and heavy-tailed behavior. Consequently, we employ a stable distribution that models variations in traffic rate residuals with very high accuracy. Finally, we propose an online detection mechanism that utilizes deviations from the rate residual distribution of blackhole traffic data to detect malware outbreaks. Experimental results using the IMS data for approximately one year show that the proposed mechanism accurately detects malware outbreaks in a timely manner.
Starting Page	1593
Ending Page	1597
File Size	160493
Page Count	5
File Format	PDF
ISBN	9781424420759
DOI	10.1109/ICC.2008.308
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-05-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Traffic control Internet Forensics Robustness Stochastic processes Computer worms Communications Society Computer science USA Councils Information technology
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
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
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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