NDLI: Recursive data mining for masquerade detection and author identification

Content Provider	IEEE Xplore Digital Library
Author	Szymanski, B.K. Zhang, Y.
Copyright Year	2003
Description	Author affiliation: Dept. of Comput. Sci., Rensselaer Polytech. Inst., Troy, NY, USA (Szymanski, B.K.; Zhang, Y.)
Abstract	In this paper, a novel recursive data mining method based on the simple but powerful model of cognition called a conceptor is introduced and applied to computer security. The method recursively mines a string of symbols by finding frequent patterns, encoding them with unique symbols and rewriting the string using this new coding. We apply this technique to two related but important problems in computer security: (i) masquerade detection to prevent a security attack in which an intruder impersonates a legitimate user to gain access to the resources, and (ii) author identification, in which anonymous or disputed computer session needs to be attributed to one of a set of potential authors. Many methods based on automata theory, hidden Markov models, Bayesian models or even matching algorithms from bioinformatics have been proposed to solve the masquerading detection problem but less work has been done on the author identification. We used recursive data mining to characterize the structure and high-level symbols in user signatures and the monitored sessions. We used one-class SVM to measure the similarity of these two characterizations. We applied weighting prediction scheme to author identification. On the SEA dataset that we used in our experiments, the results were very promising.
Sponsorship	The Nat. Security Agency IEEE Syst., Man and Cybernetics Soc., and The Dept. of Electr. and Comput. Sci. of the US Military Acad
Starting Page	424
Ending Page	431
File Size	1700389
Page Count	8
File Format	PDF
ISBN	0780385721
DOI	10.1109/IAW.2004.1437848
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-06-10
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data mining Computer security Hidden Markov models Cognition Encoding Automata Bayesian methods Bioinformatics Monitoring Support vector machines
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	Mr. Siddhartha Mukherjee siddhartha@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	ndl-support@iitkgp.ac.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Libarray 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

A Study of Effectiveness in Masquerade Detection

Recursive Data Mining for Masquerade Detection and Author Identification

Masquerade Detection Using String Kernels

Profiling Users in GUI Based Systems for Masquerade Detection

Semantic Event Extraction Using Neural Network Ensembles

Detecting queues at vending machines: A statistical layered approach

Practical User Identification for Masquerade Detection

Semantic Event Detection using Conditional Random Fields

Shot and Scoring Events Identification of Basketball Videos

Recursive data mining for masquerade detection and author identification

Similar Documents

A Study of Effectiveness in Masquerade Detection

Recursive Data Mining for Masquerade Detection and Author Identification

Masquerade Detection Using String Kernels

Profiling Users in GUI Based Systems for Masquerade Detection

Semantic Event Extraction Using Neural Network Ensembles

Detecting queues at vending machines: A statistical layered approach

Practical User Identification for Masquerade Detection

Semantic Event Detection using Conditional Random Fields

Shot and Scoring Events Identification of Basketball Videos

Recursive data mining for masquerade detection and author identification