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General Information
    • ISSN: 2301-3559
    • Frequency: Quarterly
    • DOI: 10.18178/LNSE
    • Editor-in-Chief: Prof. Jemal Antidze
    • Executive Editor: Ms. Nina Lee
    • Abstracting/ Indexing: EI (INSPEC, IET), Electronic Journals Library,  Ulrich's Periodicals Directory, International Computer Science Digital Library (ICSDL), ProQuest and Google Scholar.
    • E-mail: lnse@ejournal.net
Prof. Jemal Antidze
I. Vekua Scientific Institute of Applied Mathematics
Tbilisi State University, Georgia
I'm happy to take on the position of editor in chief of LNSE. We encourage authors to submit papers concerning any branch of Software Engineering.

LNSE 2013 Vol.1(1): 49-52 ISSN:2301-3559
DOI: 10.7763/LNSE.2013.V1.11

Internal Attacker Detection by Analyzing User Keystroke Credential

Dwijen Rudrapal, Smita Das, Nikhil Debbarma, and Swapan Debbarma

Abstract—Considering the recent Internet-based attack, many organizations are so focused on firewall and web server security. As a result, many organizations fail to realize the looming threat of attack from the inside. Based on recent few studies it could be said that insider attacks represent the most prevalent of all threat. More complicated issue is that insider attackers are not detectable with only the classic cryptography based techniques. In an organization employees used to login by ID and password. But password is not enough strong to maintain its privacy in this era of technology. In this paper the proposed algorithm considers multiple attributes of user keystroke dynamics which in addition of traditional authentication has applied in an organization for distinguishing one user than another. In earlier studies of keystrokes, we observed degree of disorder for keystroke duration and latency and their uniqueness. Along with standard deviation of keystroke duration feature, we proposed this algorithm which helps to find an internal attacker easily. More over it is done without letting inform any user and without use of any extra hardware. The obtained experiment result disclose that even though internal attacks can’t be eliminated but based on keystroke dynamics values internal attackers can be identified.

Index Terms—Keystroke dynamics, biometrics, degree of disorder, digraph, key-pressed duration, key flight time.

The authors are with the Dept. of Computer Sc. & Engineering, National Institute of Technology, Agartala Jirania, Tripura, India. (e-mail: dwijen.rudrapal@gmail.com, smitadas.nita@gmail.com, dwijen.rudrapal@gmail.com, smitadasnita.agt@gmail.com).


Cite: Dwijen Rudrapal, Smita Das, Nikhil Debbarma, and Swapan Debbarma, "Internal Attacker Detection by Analyzing User Keystroke Credential," Lecture Notes on Software Engineering  vol. 1, no. 1, pp. 49-52, 2013.

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