NDLI: On the Use of SVMs to Detect Anomalies in a Stream of SIP Messages

Content Provider	IEEE Xplore Digital Library
Author	Ferdous, R. Cigno, R.L. Zorat, A.
Copyright Year	2012
Abstract	Voice and multimedia communications are rapidly migrating from traditional networks to TCP/IP networks (Internet), where services are provisioned by SIP (Session Initiation Protocol). This paper proposes an on-line filter that examines the stream of incoming SIP messages and classifies them as good or bad. The classification is carried out in two stages: first a lexical analysis is performed to weed out those messages that do not belong to the language generated by the grammar defined by the SIP standard. After this first stage, a second filtering occurs which identifies messages that somehow differ - in structure or contents - from messages that were previously classified as good. While the first filter stage is straightforward, as the classification is crisp (either a messages belongs to the language or it does not), the second stage requires a more delicate handling, as it is not a sharp decision whether a message is semantically meaningful or not. The approach we followed for this step is based on using past experience on previously classified messages, i.e. a "learn-by-example" approach, which led to a classifier based on Support-Vector-Machines (SVM) to perform the required analysis of each incoming SIP message. The paper describes the overall architecture of the two-stage filter and then explores several points of the configuration-space for the SVM to determine a good configuration setting that will perform well when used to classify a large sample of SIP messages obtained from real traffic collected on a VoIP installation at our institution. Finally, the performance of the classification on additional messages collected from the same source is presented.
Starting Page	592
Ending Page	597
File Size	368531
Page Count	6
File Format	PDF
ISBN	9781467346511
DOI	10.1109/ICMLA.2012.109
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-12-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Protocols Accuracy Syntactics VoIP Polynomials Servers Kernel Support Vector Machines Session Initiation Protocol
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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