ICTACT Journal on Soft Computing

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Performance Analysis of Anomalous Detection Scehmes Based on Modified Support Vector Machine and Enhanced Relevance Vector Machine


Affiliations
1 Department of Computer Science, Kongunadu Arts and Science College, India
2 Department of Information Technology, Kongunadu Arts and Science College, India
     

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Anomalous transactions are common activity happening on the financial oriented transaction. Detecting those anomalous transactions from the financial transaction patterns is the most complex task which is focused in this work. In the existing work it is achieved by introducing the method namely Fuzzy Exception and Fuzzy Anomalous Rule (FEFAR). The accuracy of this existing work FEFAR found to be lesser which is resolved in the proposed research work. There are two research works has been proposed those are namely Rule Pruning based Anomalous Rule Detection Strategy (RPARD) and Lasso Regression based Improved Anomalous Detection Scheme (LR-IADS). Both of these methods attempt to find the anomalous transaction from the given input database by finding the anomalous rules. Each method differ in its methodologies, thus the accuracy of the methods would differ. The main goal of this analysis work is to compare the performance of existing and proposed methodologies based on simulation outcome. This research work aims to highlight the performance variation between the proposed and existing techniques and the best method that can offer accurate anomalous transaction detection. The analysis of the research work is carried out on matlab environment over four databases namely soil, bank, german statlog and auto mpg based on which performance outcome has been given.

Keywords

Anomalous Transaction, Anomalous Rules, Accuracy, RPARD, LR-IADS, FEFAR.
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  • T. Watanabe, A. Kitamura, K. Higuchi and H. Ikeda, “Intelligent Manufacturing Database Techniques for Quality and Process Design of Steel Plate”, Proceedings of IEEE Conference on Emerging Technologies and Factory Automation, pp. 596-603, 2003.

  • R. Srikant and R. Agrawal, “Mining Generalized Association Rules”, Proceedings of IEEE Conference on Very Large Data Bases, pp. 407-419, 1995.

  • R. Srikant and R. Agrawal, “Mining Quantitative Association Rules in Large Relational Tables”, Proceedings of IEEE Conference on Management of the Data, pp.1-12, 1996.

  • G. Chen and Q. Wei, “Fuzzy Association Rules and the Extended Mining Algorithms”, Information Sciences, Vo1. l47, No. 1-4, pp. 221-228, 2002.

  • H. Ishibuchi and T. Yamamoto, “Fuzzy Rule Selection by Data Mining Criteria and Genetic Algorithms”, Proceedings of Annual Conference on Genetic and Evolutionary Computation, pp. 399-406, 2002.

  • Y. Hu, R. Chen and G. Tzeng, “Discovering Fuzzy Association Rules Using Fuzzy Partition Methods”, Knowledge-Based Systems, Vol. 16, No. 3, pp. 137-147, 2003.

  • T. Watanabe and N. Nakayama, “Fuzzy Rule Extraction Based on the Mining Generalized Association Rules”, Proceedings of IEEE International Conference on Systems, Man and Cybernetics. Conference Theme-System Security and Assurance, pp. 2690-2695, 2003.

  • M. Delgado, N. Marin, D. Sanchez and M.A. Vila, “Fuzzy Association Rules: General Model and Applications”, IEEE transactions on Fuzzy Systems, Vol 11, No. 2, pp. 214-225, 2003.

  • M. Delgado, N. Marin, M.J. Martin Bautista, D. Sanchez and M.A. Vila, “Mining Fuzzy Association Rules: An Overview”, Proceedings of IEEE International Conference on Soft Computing for Information Processing and Analysis, pp. 351-373, 2006.

  • E. Suzuki, “Discovering Unexpected Exceptions: A Stochastic Approach”, Proceedings of IEEE International Conference on Rough Sets, Fuzzy Sets, and Machine Discovery, pp. 225-232, 1996.

  • F. Berzal, J.C. Cubero, N. Marn and M. Gamez, “Anomalous association rules”, Proceedings of IEEE International Conference on Alternative Techniques for Data Mining and Knowledge Discovery, pp. 1-8, 2004.

  • M. Delgado, M.D. Ruiz and D. Sanchez, “New Approaches for Discovering Exception and Anomalous Rules”, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Vol. 19, No.2, pp. 361-399, 2011.

  • T. Watanabe and R. Fujioka, “Fuzzy Association Rules Mining Algorithm Based on Equivalence Redundancy of Items”, Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, pp.1960-1965, 2012.

  • E. Suzuki, “Autonomous Discovery of Reliable Exception Rules”, Proceedings of International Conference on Knowledge Discovery and Data Mining, pp. 159-176, 1997.

  • M.D. Ruiz, D. Snchez, M. Delgado and M.J. Martin Bautista, “Discovering Fuzzy Exception and Anomalous Rules”, IEEE Transactions on Fuzzy Systems, Vol. 24, No. 4, pp. 930-944, 2016.

  • E. Suzuki, “Data Mining Methods for Discovering Interesting Exceptions from an Unsupervised Table”, Journal of Universal Computer Science, Vol. 12, No. 6, pp. 627-653, 2006.

  • E. Suzuki and J.M. Zytkow, “Unified Algorithm for Undirected Discovery of Exception Rules”, International Journal of Intelligent Systems, Vol. 20, No. 7, pp. 673-691, 2005.

  • T. Zhang, W. Zhang, X.U. Wei and H.A.O. Haijing, “Multiple Instance Learning for Credit Risk Assessment with Transaction Data”, Knowledge-Based Systems, Vol. 161, pp. 65-77, 2018.

  • S. Senthil Kumar and S. Mythili, “Survey on Exception Rules and Anomaly Detection”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, Vol. 2, No. 6, pp.521- 525, 2017.

  • S. Senthil Kumar and S. Mythili, “Accurate Fuzzy Anomalous Rule Identification Using Classification Algorithms”, Journal of Advance Research in Dynamical and Control Systems, Vol. 11, No. 5, pp. 241-262, 2019.

  • S. Chen, M. Peng, H. Xiong and S. Wu, “An Anomaly Detection Method based on Lasso”, Cluster Computing, Vol. 22, No. 3, pp.5407-5419, 2019.


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  • Performance Analysis of Anomalous Detection Scehmes Based on Modified Support Vector Machine and Enhanced Relevance Vector Machine

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Authors

S. Senthil Kumar
Department of Computer Science, Kongunadu Arts and Science College, India
S. Mythili
Department of Information Technology, Kongunadu Arts and Science College, India

Abstract


Anomalous transactions are common activity happening on the financial oriented transaction. Detecting those anomalous transactions from the financial transaction patterns is the most complex task which is focused in this work. In the existing work it is achieved by introducing the method namely Fuzzy Exception and Fuzzy Anomalous Rule (FEFAR). The accuracy of this existing work FEFAR found to be lesser which is resolved in the proposed research work. There are two research works has been proposed those are namely Rule Pruning based Anomalous Rule Detection Strategy (RPARD) and Lasso Regression based Improved Anomalous Detection Scheme (LR-IADS). Both of these methods attempt to find the anomalous transaction from the given input database by finding the anomalous rules. Each method differ in its methodologies, thus the accuracy of the methods would differ. The main goal of this analysis work is to compare the performance of existing and proposed methodologies based on simulation outcome. This research work aims to highlight the performance variation between the proposed and existing techniques and the best method that can offer accurate anomalous transaction detection. The analysis of the research work is carried out on matlab environment over four databases namely soil, bank, german statlog and auto mpg based on which performance outcome has been given.

Keywords


Anomalous Transaction, Anomalous Rules, Accuracy, RPARD, LR-IADS, FEFAR.

References