Abstract
Neural Networks are used together with fuzzy inference systems in Neuro-Fuzzy, a prominent synergy of rules parameters unsupervised discovery and supervised tuning of classification model. The binary classification task in Digital Forensics applications are the most widely used and applied for detection “benign” and “malicious” activities. However, in many areas it is not enough to distinguish between those two classes, yet also important to provide a more specific determination of what exactly “malicious” sub-class some action belongs to. Despite the inherited properties and limitation of Neural Networks, the Neuro-Fuzzy may be tuned to handle non-linear data in multinomial classification problems, which is not a simple addition to a binary classification model. This work targets the optimization of the Neuro-Fuzzy output layer construction and rules tuning in multi-class problems as well as solving accompanying challenges.
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References
Chen, B.: The fundamentals—fuzzy system. Mamdani fuzzy models. http://www.bindichen.co.uk/post/AI/mamdani-fuzzy-model.html (September 2013). Accessed 15 Aug 2015
Kosko, B.: Fuzzy Engineering. No. v. 1 in Fuzzy Engineering. Prentice Hall, New Jersey (1997)
Aly, M.: Survey on multiclass classification methods. Neural Netw. 1–9 (2005)
Ou, G., Murphey, Y.L.: Multi-class pattern classification using neural networks. Pattern Recogn. 40(1), 4–18 (2007)
Madzarov, G., Gjorgjevikj, D.: Multi-class classification using support vector machines in decision tree architecture. In: EUROCON 2009, EUROCON ‘09. IEEE. pp. 288–295 (2009)
Shah, K., Dave, N., Chavon, S.: Adaptive neuro-fuzzy intrusion detection system. In: Proceeding IEEE International Conference Information Technology: Coding and Computing (2004)
Sindal, R., Tokekar, S.: Adaptive soft handoff based neuro-fuzzy call admission control scheme for multiclass calls in cdma cellular network. In: Recent Advances in Information Technology (RAIT), 2012 1st International Conference on. pp. 279–284 (2012)
Lin, Y.H., Tsai, M.S.: Non-intrusive load monitoring by novel neuro-fuzzy classification considering uncertainties. IEEE Trans. Smart Grid 5(5), 2376–2384 (2014)
Eiamkanitchat, N., Theera-Umpon, N., Auephanwiriyakul, S.: A novel neuro-fuzzy method for linguistic feature selection and rule-based classification. In: The 2nd International Conference on Computer and Automation Engineering (ICCAE), 2010, vol. 2, pp. 247–252 (2010)
Guo, N.R., Kuo, C.L., Tsai, T.J.: Design of an ep-based neuro-fuzzy classification model. In: International Conference on Networking, Sensing and Control, 2009. ICNSC ‘09, pp. 918–923 (2009)
Shalaginov, A., Franke, K.: A new method for an optimal som size determination in neuro-fuzzy for the digital forensics applications. In: Advances in Computational Intelligence, pp. 549–563. Springer International Publishing, Berlin (2015)
Shalaginov, A., Franke, K.: A new method of fuzzy patches construction in neuro-fuzzy for malware detection. In: IFSA-EUSFLAT. Atlantis Press, Amsterdam (2015)
Dickerson, J.A., Kosko, B.: Fuzzy function approximation with ellipsoidal rules. Trans. Sys. Man Cyber. Part B 26(4), 542–560 (1996)
Alahakoon, D., Halgamuge, S., Srinivasan, B.: A self-growing cluster development approach to data mining. In: Systems, Man, and Cybernetics, 1998. 1998 IEEE International Conference on. vol. 3, pp. 2901–2906 (1998)
Ross, T.: Fuzzy Logic with Engineering Applications. Wiley, Hoboken (2009)
Chen, C.H., Li, K.C.: A three-way classification strategy for reducing class-abundance: the zip code recognition example. Lecture Notes-Monograph Series pp. 63–86 (2004)
Allwein, E.L., Schapire, R.E., Singer, Y.: Reducing multiclass to binary: a unifying approach for margin classifiers. J. Mach. Learn. Res. 1, 113–141 (2001)
Hurwitz, J.S.: Error-correcting codes and applications to large scale classification systems. Ph.D. thesis, Massachusetts Institute of Technology (2009)
Pachopoulos, K., Valsamou, D., Mavroeidis, D., Vazirgiannis, M.: Feature extraction from web traffic data for the application of data mining algorithms in attack identification. In: Proceedings of the ECML/PKDD. pp. 65–70 (2007)
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The authors would like to acknowledge the sponsorship and support from COINS Research School of Computer and Information Security.
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Shalaginov, A., Franke, K. (2016). Towards Improvement of Multinomial Classification Accuracy of Neuro-Fuzzy for Digital Forensics Applications. In: Abraham, A., Han, S., Al-Sharhan, S., Liu, H. (eds) Hybrid Intelligent Systems. HIS 2016. Advances in Intelligent Systems and Computing, vol 420. Springer, Cham. https://doi.org/10.1007/978-3-319-27221-4_17
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