IEEE Transactions on Software Engineering - Special issue on computer security and privacy
Authentication via keystroke dynamics
Proceedings of the 4th ACM conference on Computer and communications security
Temporal sequence learning and data reduction for anomaly detection
ACM Transactions on Information and System Security (TISSEC)
Enhancing profiles for anomaly detection using time granularities
Journal of Computer Security
Intrusion Detection via Static Analysis
SP '01 Proceedings of the 2001 IEEE Symposium on Security and Privacy
Fixed-point GMM-based speaker verification over mobile embedded system
WBMA '03 Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications
User re-authentication via mouse movements
Proceedings of the 2004 ACM workshop on Visualization and data mining for computer security
Efficient fingerprint-based user authentication for embedded systems
Proceedings of the 42nd annual Design Automation Conference
Architectures for efficient face authentication in embedded systems
Proceedings of the conference on Design, automation and test in Europe: Designers' forum
An examination of user behavior for user re-authentication
An examination of user behavior for user re-authentication
A new iris recognition approach for embedded system
ICESS'04 Proceedings of the First international conference on Embedded Software and Systems
Touch me once and i know it's you!: implicit authentication based on touch screen patterns
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Back-of-device authentication on smartphones
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Efficient location aware intrusion detection to protect mobile devices
Personal and Ubiquitous Computing
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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.