NDLI: Negative Selection with High-Dimensional Support for Keystroke Dynamics

Content Provider	IEEE Xplore Digital Library
Author	Pisani, P.H. Lorena, A.C.
Copyright Year	2012
Abstract	Computing and communication systems have been expanding and bringing a number of advancements to our way of life. However, this technological evolution has also contributed to the rise of the identity theft, mainly due to the advent of the digital identity. An alternative to overcome this problem is by the analysis of the user behavior, known as behavioral intrusion detection. Among the possible aspects to be analysed, this work focuses on the keystroke dynamics, which consists of recognizing users by their typing rhythm. This paper draws a comparison between some novelty detectors applied to keystroke dynamics: immune negative selection algorithms and auto-associative neural networks. Issues regarding the use of negative selection in high dimensional spaces are discussed and an alternative to deal with this problem is presented.
Starting Page	19
Ending Page	24
File Size	194003
Page Count	6
File Format	PDF
ISBN	9781467326414
ISSN	15224899
DOI	10.1109/SBRN.2012.15
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-10-20
Publisher Place	Brazil
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Detectors Training Heuristic algorithms Algorithm design and analysis Databases Accuracy Feature extraction negative selection keystroke dynamics artificial immune systems
Content Type	Text
Resource Type	Article

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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
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