User and task analysis for interface design
User and task analysis for interface design
Temporal sequence learning and data reduction for anomaly detection
CCS '98 Proceedings of the 5th ACM conference on Computer and communications security
Communications of the ACM
Evolving rule-based models: a tool for design of flexible adaptive systems
Evolving rule-based models: a tool for design of flexible adaptive systems
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Automatically sharing web experiences through a hyperdocument recommender system
Proceedings of the fourteenth ACM conference on Hypertext and hypermedia
Intrusion Detection: A Bioinformatics Approach
ACSAC '03 Proceedings of the 19th Annual Computer Security Applications Conference
User Profiling for Web Page Filtering
IEEE Internet Computing
A daily behavior enabled hidden Markov model for human behavior understanding
Pattern Recognition
Removing biases in unsupervised learning of sequential patterns
Intelligent Data Analysis
Sequence classification using statistical pattern recognition
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
Evolving Fuzzy-Rule-Based Classifiers From Data Streams
IEEE Transactions on Fuzzy Systems
User modelling for exclusion and anomaly detection: a behavioural intrusion detection system
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
ALERT-ID: analyze logs of the network element in real time for intrusion detection
RAID'12 Proceedings of the 15th international conference on Research in Attacks, Intrusions, and Defenses
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Knowledge about computer users is very beneficial for assisting them, predicting their future actions or detecting masqueraders. In this paper, an approach for creating and recognizing automatically the behavior profile of a user from the commands (s)he types in a command-line interface, is presented. Specifically, in this research, a computer user behavior is represented as a sequence of UNIX commands. This sequence is transformed into a distribution of relevant subsequences in order to find out a profile that defines its behavior. Then, statistical methods are used for recognizing a user from the commands (s)he types. The experiment results, using 2 different sources of UNIX command data, show that a system based on our approach can efficiently recognize a UNIX user. In addition, a comparison with a HMM-base method is done. Because a user profile usually changes constantly, we also propose a method to keep up to date the created profiles using an age -based mechanism.