@article{iglesias11TKDE,
    author  = {Jose Antonio Iglesias and Plamen Angelov and Agapito Ledezma and Araceli Sanchis},
    title = {Creating Evolving User Behavior Profiles Automatically},
    journal = {IEEE Transactions on Knowledge and Data Engineering},
    year = {2011},
    note = {In Press},
    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.},
    issn = {1041-4347}
}

{"_id":{"_str":"5342a9600e946d920a002f7e"},"__v":1,"authorIDs":[],"author_short":["Iglesias, J. A.","Angelov, P.","Ledezma, A.","Sanchis, A."],"bibbaseid":"iglesias-angelov-ledezma-sanchis-creatingevolvinguserbehaviorprofilesautomatically-2011","bibdata":{"bibtype":"article","type":"article","author":[{"firstnames":["Jose","Antonio"],"propositions":[],"lastnames":["Iglesias"],"suffixes":[]},{"firstnames":["Plamen"],"propositions":[],"lastnames":["Angelov"],"suffixes":[]},{"firstnames":["Agapito"],"propositions":[],"lastnames":["Ledezma"],"suffixes":[]},{"firstnames":["Araceli"],"propositions":[],"lastnames":["Sanchis"],"suffixes":[]}],"title":"Creating Evolving User Behavior Profiles Automatically","journal":"IEEE Transactions on Knowledge and Data Engineering","year":"2011","note":"In Press","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.","issn":"1041-4347","bibtex":"@article{iglesias11TKDE,\r\n author = {Jose Antonio Iglesias and Plamen Angelov and Agapito Ledezma and Araceli Sanchis},\r\n title = {Creating Evolving User Behavior Profiles Automatically},\r\n journal = {IEEE Transactions on Knowledge and Data Engineering},\r\n year = {2011},\r\n note = {In Press},\r\n 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.},\r\n issn = {1041-4347}\r\n}\r\n\r\n","author_short":["Iglesias, J. A.","Angelov, P.","Ledezma, A.","Sanchis, A."],"key":"iglesias11TKDE","id":"iglesias11TKDE","bibbaseid":"iglesias-angelov-ledezma-sanchis-creatingevolvinguserbehaviorprofilesautomatically-2011","role":"author","urls":{},"downloads":0,"html":"","metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"http://www.caos.inf.uc3m.es/bibs/pubCAOS.bib","downloads":0,"keywords":[],"search_terms":["creating","evolving","user","behavior","profiles","automatically","iglesias","angelov","ledezma","sanchis"],"title":"Creating Evolving User Behavior Profiles Automatically","year":2011,"dataSources":["Z4T4fewybNpYCWveD"]}