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Detecting Masqueraders using user Behavior Profiles


Affiliations
1 Department of Computer Science and Engineering, R.M.D. Engineering College, Chennai-601 206, India
     

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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 she/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 online 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

User Behavior Profiles, Masqueraders, Command Interrupter.
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  • Detecting Masqueraders using user Behavior Profiles

Abstract Views: 230  |  PDF Views: 2

Authors

V. Nandakumar
Department of Computer Science and Engineering, R.M.D. Engineering College, Chennai-601 206, India
S. Muthusundari
Department of Computer Science and Engineering, R.M.D. Engineering College, Chennai-601 206, India

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 she/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 online 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


User Behavior Profiles, Masqueraders, Command Interrupter.



DOI: https://doi.org/10.36039/ciitaas%2F5%2F3%2F2013%2F106841.122-125