NDLI: Probabilistic Neural Network based attack traffic classification

Content Provider	IEEE Xplore Digital Library
Author	Akilandeswari, V. Shalinie, S.M.
Copyright Year	2012
Description	Author affiliation: Department of Computer Science Engineering, Thiagarajar College of Engineering, Madurai, 625010, Tamil Nadu (Akilandeswari, V.; Shalinie, S.M.)
Abstract	This paper surveys with the emerging research on various methods to identify the legitimate/illegitimate traffic on the network. Here, the focus is on the effective early detection scheme for distinguishing Distributed Denial of Service (DDoS) attack traffic from normal flash crowd traffic. The basic characteristics used to distinguish Distributed Denial of Service (DDoS) attacks from flash crowds are access intents, client request rates, cluster overlap, distribution of source IP address, distribution of clients and speed of traffic. Various techniques related to these metrics are clearly illustrated and corresponding limitations are listed out with their justification. A new method is proposed in this paper which builds a reliable identification model for flash crowd and DDoS attacks. The proposed Probabilistic Neural Network based traffic pattern classification method is used for effective classification of attack traffic from legitimate traffic. The proposed technique uses the normal traffic profile for their classification process which consists of single and joint distribution of various packet attributes. The normal profile contains uniqueness in traffic distribution and also hard for the attackers to mimic as legitimate flow. The proposed method achieves highest classification accuracy for DDoS flooding attacks with less than 1% of false positive rate.
Starting Page	1
Ending Page	8
File Size	431302
Page Count	8
File Format	PDF
ISBN	9781467355834
e-ISBN	9781467355841
DOI	10.1109/ICoAC.2012.6416848
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-12-13
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement Attack Traffic Classification Flash Crowd Event Probabilistic Neural Network DDoS attacks Neural networks Probabilistic logic Internet IP networks Servers Computer crime
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