TY - GEN
T1 - Implicit user re-authentication for mobile devices
AU - Yazji, Sausan
AU - Chen, Xi
AU - Dick, Robert P.
AU - Scheuermann, Peter
N1 - Funding Information:
This work was supported in part by the National Science Foundation under awards CNS-0347941, CNS-0720691, and CNS-0613967.
PY - 2009
Y1 - 2009
N2 - Portable computers are used to store and access sensitive information. They are frequently used in insecure locations with little or no physical protection, and are therefore susceptible to theft and unauthorized access. We propose an implicit user re-authentication system for portable computers that requires no application changes or hardware modifications. The proposed technique observes user-specific patterns in filesystem activity and network access to build models of normal behavior. These are used to distinguish between normal use and anomalous use. We describe these automated model generation and user detection techniques, and explain how to efficiently implement them in a wireless distributed system composed of servers and battery-powered portable devices. The proposed system is able to distinguish between normal use and attack with an accuracy of approximately 90% every 5 minutes and consumes less than 12% of a typical laptop battery in 24 hours.
AB - Portable computers are used to store and access sensitive information. They are frequently used in insecure locations with little or no physical protection, and are therefore susceptible to theft and unauthorized access. We propose an implicit user re-authentication system for portable computers that requires no application changes or hardware modifications. The proposed technique observes user-specific patterns in filesystem activity and network access to build models of normal behavior. These are used to distinguish between normal use and anomalous use. We describe these automated model generation and user detection techniques, and explain how to efficiently implement them in a wireless distributed system composed of servers and battery-powered portable devices. The proposed system is able to distinguish between normal use and attack with an accuracy of approximately 90% every 5 minutes and consumes less than 12% of a typical laptop battery in 24 hours.
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U2 - 10.1007/978-3-642-02830-4_25
DO - 10.1007/978-3-642-02830-4_25
M3 - Conference contribution
AN - SCOPUS:70350654051
SN - 3642028292
SN - 9783642028298
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 325
EP - 339
BT - Ubiquitous Intelligence and Computing - 6th International Conference, UIC 2009, Proceedings
T2 - 6th International Conference on Ubiquitous Intelligence and Computing, UIC 2009
Y2 - 7 July 2009 through 9 July 2009
ER -