Abstract
The complexity of the fuzzy classification models in Digital Forensics is considered to be one of the most significant aspects that influence a decision making process. We focus on criteria for an optimal SOM size and amount of rules to be derived that results in accurate and interpretable model. In this paper, we proposed a new method for the SOM size determination based on the data exploratory analysis. Experiments showed that the proposed method gives an accuracy on the Android malware detection up to 92% while decreasing the number of recommended rules from 189 to 24 in comparison to Vesanto method for an optimal SOM size. This is an important step for automated training of Neuro-Fuzzy that will result in human-understandable model that will be used in Digital Forensics process.
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References
Ahn, S.C., Horenstein, A.R.: Eigenvalue ratio test for the number of factors. Econometrica 81(3), 1203–1227 (2013)
Alahakoon, D., Halgamuge, S., Srinivasan, B.: Dynamic self-organizing maps with controlled growth for knowledge discovery. IEEE Transactions on Neural Networks 11(3), 601–614 (2000)
Alahakoon, D., Halgamuge, S., Srinivasan, B.: A self-growing cluster development approach to data mining. In: 1998 IEEE International Conference on Systems, Man, and Cybernetics, vol. 3, pp. 2901–2906, October 1998
Alonso, J.M., Cordn, O., Quirin, A., Magdalena, L.: Analyzing interpretability of fuzzy rule-based systems by means of fuzzy inference-grams. In: World Congress on Soft Computing (2011)
Altyeb Altaher, A.A., Ramadass, S.: Application of adaptive neuro-fuzzy inference system for information secuirty. Journal of Computer Science 8(6), 983–986 (2012)
Baltimore, R.: An Analytic investigation into self organizing maps and their network topologies. Ph.D. thesis, Rochester Institute of Technology (2010)
Castellano, G., Fanelli, A.M., Mencar, C.: Discovering interpretable classification rules from neural processed data (2002)
Chattopadhyay, M., Dan, P.K., Mazumdar, S.: Application of visual clustering properties of self organizing map in machine-part cell formation. Appl. Soft Comput. 12(2), 600–610 (2012). http://dx.doi.org/10.1016/j.asoc.2011.11.004
Clark, M.: A comparison of correlation measures. University of Notre Dame, Tech. rep. (2013)
Dickerson, J.A., Kosko, B.: Fuzzy function approximation with ellipsoidal rules. Trans. Sys. Man Cyber. Part B 26(4), 542–560 (1996). http://dx.doi.org/10.1109/3477.517030
Estévez, P., Príncipe, J., Zegers, P.: Advances in Self-Organizing Maps: 9th International Workshop, WSOM 2012. AISC, vol. 198. Springer, Heidelberg (2012). https://books.google.no/books?id=vHgnfKFpIFUC
Feldman, E.R.: Criteria for admissibility of expert opinion testimony under daubert and its progeny. Tech. rep, Cozen OConnor (2001)
Gacto, M., Alcal, R., Herrera, F.: Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures. Information Sciences 181(20), 4340–4360 (2011). http://www.sciencedirect.com/science/article/pii/S0020025511001034, special Issue on Interpretable Fuzzy Systems
Guarino, A.: Digital forensics as a big data challenge. In: ISSE 2013 Securing Electronic Business Processes, pp. 197–203. Springer (2013)
Hasan, S., Shamsuddin, S.M.: Multistrategy self-organizing map learning for classification problems. Computational Intelligence and Neuroscience 2011, 11 (2011)
Herrera, L., Pomares, H., Rojas, I., Valenzuela, O., Prieto, A.: Tase, a taylor series-based fuzzy system model that combines interpretability and accuracy. Fuzzy Sets and Systems 153(3), 403–427 (2005). http://www.sciencedirect.com/science/article/pii/S0165011405000333
Ishibuchi, H., Nojima, Y.: Discussions on interpretability of fuzzy systems using simple examples (2009)
Jin, Y.: Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement. IEEE Transactions on Fuzzy Systems 8(2), 212–221 (2000)
Kosko, B.: Fuzzy Engineering. No. v. 1 in Fuzzy Engineering. Prentice Hall (1997). http://books.google.no/books?id=8QwoAQAAMAAJ
Martínez-Gómez, E., Richards, M.T., Richards, D.S.P.: Distance correlation methods for discovering associations in large astrophysical databases. The Astrophysical Journal 781, 39 (2014)
Piegat, A.: Fuzzy Modeling and Control. STUDFUZZ, vol. 69. Physica-Verlag, Heidelberg (2001). http://books.google.no/books?id=329oSfh-vxsC
Fei Qiao, J., Gui Han, H.: An Adaptive Fuzzy Neural Network Based on Self-Organizing Map (SOM). INFTECH, April 2010, iSBN 978-953-307-074-2
Schmidt, C.R.: Effect of irregular topology in shperical Self-Organizing Maps. Ph.D. thesis, San Diego State University (December 2008)
Singh, R., Kumar, H., Singla, R.: Review of soft computing in malware detection. Special Issues on IP Multimedia Communications (1), 55–60 (2011), full text available
Smith, L.I.: A tutorial on principal components analysis. Tech. rep., Cornell University, USA (February 26, 2002). http://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf
Szkely, J., Rizzo, M.L., Bakirov, N.K.: Measuring and testing dependence by correlation of distances
Vesanto, J., Alhoniemi, E.: Clustering of the self-organizing map (2000)
Vesanto, J., Himberg, J., Alhoniemi, E., Parhankangas, J.: Self-organizing map in matlab: the som toolbox. In: Proceedings of the Matlab DSP Conference, pp. 35–40 (2000)
Zhang, Y. (ed.) Machine Learning. INFTECH (February 2010), isbn 978-953-307-033-9
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Shalaginov, A., Franke, K. (2015). A New Method for an Optimal SOM Size Determination in Neuro-Fuzzy for the Digital Forensics Applications. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2015. Lecture Notes in Computer Science(), vol 9095. Springer, Cham. https://doi.org/10.1007/978-3-319-19222-2_46
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DOI: https://doi.org/10.1007/978-3-319-19222-2_46
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