A New Method for an Optimal SOM Size Determination in Neuro-Fuzzy for the Digital Forensics Applications

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.