Computer-Access Security Systems Using Keystroke Dynamics
IEEE Transactions on Pattern Analysis and Machine Intelligence
User identification via keystroke characteristics of typed names using neural networks
International Journal of Man-Machine Studies
The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
Toward cost-sensitive modeling for intrusion detection and response
Journal of Computer Security
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The presence of long gaps dramatically increases the difficulty of detecting and characterizing complex events hidden in long sequences. In order to cope with this problem, a learning algorithm based on an abstraction mechanism is proposed: it can infer a Hierarchical Hidden Markov Model, from a learning set of sequences. The induction algorithm proceeds bottom-up, progressively coarsening the sequence granularity, and letting correlations between subsequences, separated by long gaps, naturally emerge. As a case study, the method is evaluated on an application of user profiling. The results show that the proposed algorithm is suitable for developing real applications in network security and monitoring.