Identity authentication based on keystroke latencies
Communications of the ACM
Computer-Access Security Systems Using Keystroke Dynamics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fundamentals of speech recognition
Fundamentals of speech recognition
User identification via keystroke characteristics of typed names using neural networks
International Journal of Man-Machine Studies
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
Data Mining and Knowledge Discovery
Toward cost-sensitive modeling for intrusion detection and response
Journal of Computer Security
Data mining approaches for intrusion detection
SSYM'98 Proceedings of the 7th conference on USENIX Security Symposium - Volume 7
The condensed nearest neighbor rule (Corresp.)
IEEE Transactions on Information Theory
The reduced nearest neighbor rule (Corresp.)
IEEE Transactions on Information Theory
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This paper presents an algorithmfor automatically constructing sophisticated user/process profiles from traces of their behavior. A profile is encoded by means of a Hierarchical Hidden Markov Model (HHMM), which is a well formalized tool suitable to model complex patterns in long temporal or spatial sequences. A special sub-class of this hierarchical model, oriented to user/process profiling, is also introduced. The algorithm follows a bottom-up strategy, in which elementary facts in the sequences (motifs) are progressively grouped, thus building the abstraction hierarchy of a HHMM, layer after layer. The method is firstly evaluated on artificial data. Then a user identification task, from real traces, is considered. A preliminary experimentation with several different users produced encouraging results.