Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Modelling evolving user behaviours
AU - Iglesias, Jose
AU - Angelov, Plamen
AU - Ledezma, Agapito
AU - Sanchis, Araceli
N1 - "©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE." "This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder."
PY - 2009/4
Y1 - 2009/4
N2 - Knowledge about computer users is very beneficial for assisting them, predicting their future actions or detecting masqueraders. In this paper, a new approach for creating and recognizing automatically the behaviour profile of a computer user is presented. In this case, a computer user behaviour is represented as the sequence of the commands (s)he types during her/his work. This sequence is transformed into a distribution of relevant subsequences of commands in order to find out a profile that defines its behaviour. Also, because of a user profile is not necessarily fixed but rather it evolves/changes, we propose an evolving method to keep up to date the created profiles using an Evolving Systems approach. In this paper we combine the evolving classifier with a trie-based user profiling to obtain a powerful self-learning on-line scheme. We also develop further the recursive formula of the potential of a data point to become a cluster centre using cosine distance which is provided in the Appendix. The novel approach proposed in this paper can be applicable to any problem of dynamic/evolving user behaviour modelling where it can be represented as a sequence of actions and events. It has been evaluated on several real data streams. (c) IEEE Press.
AB - Knowledge about computer users is very beneficial for assisting them, predicting their future actions or detecting masqueraders. In this paper, a new approach for creating and recognizing automatically the behaviour profile of a computer user is presented. In this case, a computer user behaviour is represented as the sequence of the commands (s)he types during her/his work. This sequence is transformed into a distribution of relevant subsequences of commands in order to find out a profile that defines its behaviour. Also, because of a user profile is not necessarily fixed but rather it evolves/changes, we propose an evolving method to keep up to date the created profiles using an Evolving Systems approach. In this paper we combine the evolving classifier with a trie-based user profiling to obtain a powerful self-learning on-line scheme. We also develop further the recursive formula of the potential of a data point to become a cluster centre using cosine distance which is provided in the Appendix. The novel approach proposed in this paper can be applicable to any problem of dynamic/evolving user behaviour modelling where it can be represented as a sequence of actions and events. It has been evaluated on several real data streams. (c) IEEE Press.
U2 - 10.1109/ESDIS.2009.4938994
DO - 10.1109/ESDIS.2009.4938994
M3 - Conference contribution/Paper
SN - 978-1-4244-2754-3
SP - 16
EP - 23
BT - IEEE Workshop on Evolving and Self-Developing Intelligent Systems, 2009. ESDIS '09.
PB - IEEE
T2 - IEEE Symposium Series on Computational Intelligence
Y2 - 29 March 2009 through 2 April 2009
ER -