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Creating evolving user behavior profiles automatically

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Creating evolving user behavior profiles automatically. / Iglesias, Jose; Angelov, Plamen; Ledezma, Agapito; Sanchis, Aracheli.

In: IEEE Transactions on Knowledge and Data Engineering, Vol. 24, No. 5, 01.05.2012, p. 854-867.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Iglesias, J, Angelov, P, Ledezma, A & Sanchis, A 2012, 'Creating evolving user behavior profiles automatically', IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 5, pp. 854-867. https://doi.org/10.1109/TKDE.2011.17

APA

Iglesias, J., Angelov, P., Ledezma, A., & Sanchis, A. (2012). Creating evolving user behavior profiles automatically. IEEE Transactions on Knowledge and Data Engineering, 24(5), 854-867. https://doi.org/10.1109/TKDE.2011.17

Vancouver

Iglesias J, Angelov P, Ledezma A, Sanchis A. Creating evolving user behavior profiles automatically. IEEE Transactions on Knowledge and Data Engineering. 2012 May 1;24(5):854-867. https://doi.org/10.1109/TKDE.2011.17

Author

Iglesias, Jose ; Angelov, Plamen ; Ledezma, Agapito ; Sanchis, Aracheli. / Creating evolving user behavior profiles automatically. In: IEEE Transactions on Knowledge and Data Engineering. 2012 ; Vol. 24, No. 5. pp. 854-867.

Bibtex

@article{9c3e37f7510e44eeb3b1eeda9bb66e7f,
title = "Creating evolving user behavior profiles automatically",
abstract = "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 behavior profile of a computer user is presented. In this case, a computer user behavior 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 behavior. Also, because 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 center 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 behavior modeling where it can be represented as a sequence of actions or events. It has been evaluated on several real data streams. ",
keywords = "Evolving fuzzy systems, fuzzy-rule-based (FRB) classifiers, user modeling",
author = "Jose Iglesias and Plamen Angelov and Agapito Ledezma and Aracheli Sanchis",
note = "{"}{\textcopyright}2012 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.{"}",
year = "2012",
month = may,
day = "1",
doi = "10.1109/TKDE.2011.17",
language = "English",
volume = "24",
pages = "854--867",
journal = "IEEE Transactions on Knowledge and Data Engineering",
issn = "1041-4347",
publisher = "IEEE Computer Society",
number = "5",

}

RIS

TY - JOUR

T1 - Creating evolving user behavior profiles automatically

AU - Iglesias, Jose

AU - Angelov, Plamen

AU - Ledezma, Agapito

AU - Sanchis, Aracheli

N1 - "©2012 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 - 2012/5/1

Y1 - 2012/5/1

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 behavior profile of a computer user is presented. In this case, a computer user behavior 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 behavior. Also, because 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 center 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 behavior modeling where it can be represented as a sequence of actions or events. It has been evaluated on several real data streams.

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 behavior profile of a computer user is presented. In this case, a computer user behavior 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 behavior. Also, because 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 center 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 behavior modeling where it can be represented as a sequence of actions or events. It has been evaluated on several real data streams.

KW - Evolving fuzzy systems

KW - fuzzy-rule-based (FRB) classifiers

KW - user modeling

UR - http://www.scopus.com/inward/record.url?scp=84859702329&partnerID=8YFLogxK

U2 - 10.1109/TKDE.2011.17

DO - 10.1109/TKDE.2011.17

M3 - Journal article

VL - 24

SP - 854

EP - 867

JO - IEEE Transactions on Knowledge and Data Engineering

JF - IEEE Transactions on Knowledge and Data Engineering

SN - 1041-4347

IS - 5

ER -