Towards improvement of multinomial classification accuracy of neuro-fuzzy for digital forensics applications

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.