The week's pick
Reseach Article
Knowledge Discovery from Dynamically Evolving User Profiles
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 85 - Number 1 |
| Year of Publication: 2014 |
| Authors: Md. Ahemad Pasha, R. Vijaya Prakash |
10.5120/14805-3013
|
Md. Ahemad Pasha, R. Vijaya Prakash . Knowledge Discovery from Dynamically Evolving User Profiles. International Journal of Computer Applications. 85, 1 ( January 2014), 25-29. DOI=10.5120/14805-3013
Abstract
Computer users play important role in all organizations. The users are software engineers, data entry or other kind. However having know how about the users is very useful as it helps in predicting their actions and finding whether they are compatible with their job profile, assisting them to do their job in better way and also detect adversaries or masqueraders. In this paper it analyzes computer user behavior based on the commands that they execute as part of their job profile. The commands are compared with sequence of commands associated with their profile in order to generate user behavior profiles. The profiles are evolved over a period of time and they are dynamic as the users get different positions in company and get associated with various job roles. For this reason the user behavior profiles are continually updated on order to ensure that they reflect the true knowledge of the behavior of users with respect to their job roles. It built a prototype application to demonstrate the efficiency of this approach. The empirical results revealed that this approach is useful in helping users and also monitoring their behavior in work environment and take necessary actions.
References
- D. Godoy and A. Amandi, "User Profiling in Personal InformationAgents: A Survey," Knowledge Eng. Rev. , vol. 20, no. 4, pp. 329-361, 2005.
- J. A. Iglesias, A. Ledezma, and A. Sanchis, "Creating User Profilesfrom a Command-Line Interface: A Statistical Approach," Proc. Int'l Conf. User Modeling, Adaptation, and Personalization (UMAP),pp. 90-101, 2009.
- M. Schonlau, W. Dumouchel, W. H. Ju, A. F. Karr, and Theus,"Computer Intrusion: Detecting Masquerades," Statistical Science,vol. 16, pp. 58-74, 2001.
- R. A. Maxion and T. N. Townsend, "Masquerade Detection UsingTruncated Command Lines," Proc. Int'l Conf. Dependable Systemsand Networks (DSN), pp. 219-228, 2002.
- A. Alaniz-Macedo, K. N. Truong, J. A. Camacho-Guerrero, and M. Graca-Pimentel, "Automatically Sharing Web Experiencesthrough a Hyperdocument Recommender System," Proc. ACMConf. Hypertext and Hypermedia (HYPERTEXT '03), pp. 48-56, 2003.
- D. L. Pepyne, J. Hu, and W. Gong, "User Profiling for ComputerSecurity," Proc. Am. Control Conf. , pp. 982-987, 2004.
- D. Godoy and A. Amandi, "User Profiling for Web PageFiltering," IEEE Internet Computing, vol. 9, no. 4, pp. 56-64, July/Aug. 2005.
- J. Anderson, Learning and Memory: An Integrated Approach. JohnWiley and Sons, 1995.
- Y. Horman and G. A. Kaminka, "Removing Biases in UnsupervisedLearning of Sequential Patterns," Intelligent Data Analysis,vol. 11, no. 5, pp. 457-480, 2007.
- T. Lane and C. E. Brodley, "Temporal Sequence Learning and DataReduction for Anomaly Detection," Proc. ACM Conf. Computer andComm. Security (CCS), pp. 150-158, 1998.
- P. Angelov and X. Zhou, "Evolving Fuzzy Rule-Based Classifiersfrom Data Streams," IEEE Trans. Fuzzy Systems: Special Issue onEvolving Fuzzy Systems, vol. 16, no. 6, pp. 1462-1475, Dec. 2008.
- M. Panda and M. R. Patra, "A Comparative Study of Data MiningAlgorithms for Network Intrusion Detection," Proc. Int'l Conf. Emerging Trends in Eng. and Technology, pp. 504-507, 2008.
- A. Cufoglu, M. Lohi, and K. Madani, "A Comparative Study ofSelected Classifiers with Classification Accuracy in User Profiling,"Proc. WRI World Congress on Computer Science and InformationEng. (CSIE), pp. 708-712, 2009.
- R. Polikar, L. Upda, S. S. Upda, and V. Honavar, "Learn++: AnIncremental Learning Algorithm for Supervised Neural Networks,"IEEE Trans. Systems, Man and Cybernetics, Part C (Applications and Rev. ), vol. 31, no. 4, pp. 497-508, http://dx. doi. org/10. 1109/5326. 983933, Nov. 2001.
- D. Kalles and T. Morris, "Efficient Incremental Induction ofDecision Trees," Machine Learning, vol. 24, no. 3, pp. 231-242, 1996.
- F. J. Ferrer-Troyano, J. S. Aguilar-Ruiz, and J. C. R. Santos, "DataStreams Classification by Incremental Rule Learning with ParameterizedGeneralization," Proc. ACM Symp. Applied Computing
- G. A. Carpenter, S. Grossberg, and D. B. Rosen, "Art2-a: AnAdaptive Resonance Algorithm for Rapid Category Learningand Recognition," Neural Networks, vol. 4, pp. 493-504, 1991.
- G. A. Carpenter, S. Grossberg, N. Markuzon, J. H. Reynolds, andD. B. Rosen, "Fuzzy Artmap: A Neural Network Architecture forIncremental Supervised Learning of Analog MultidimensionalMaps," IEEE Trans. Neural Networks, vol. 3, no. 5, pp. 698-713, Sept. 1992.
- N. Kasabov, "Evolving Fuzzy Neural Networks for Supervised/Unsupervised Online Knowledge-Based Learning," IEEE Trans. Systems, Man and Cybernetics—Part B: Cybernetics, vol. 31, no. 6,pp. 902-918, Dec. 2001.
- G. Widmer and M. Kubat, "Learning in the Presence of ConceptDrift and Hidden Contexts," Machine Learning, vol. 23, pp. 69-101, 1996.
- R. Xiao, J. Wang, and F. Zhang, "An Approach to IncrementalSVM Learning Algorithm," Proc. IEEE Int'l Conf. Tools withArtificial Intelligence, pp. 268-278, 2000.
- Jose Antonio Iglesias, Plamen Angelov, Agapito Ledezma and Araceli Sanchis, "Creating Evolving User Behavior Profiles Automatically". IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 24, NO. 5, MAY 2012.
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