Distributed autonomous Neuro-Gen Learning Engine for content-based document file type identification
Aaron, Opim Salim Sitompul, Romi Fadillah Rahmat · 2014
A Distributed Autonomous Neuro-Gen Learning Engine (DANGLE) is proposed in this paper for file type identification. DANGLE is a machine learning tool designed to solve limitations of existing implementation of neural networks, namely excessive training time, fixed architecture and catastrophic forgetting. DANGLE consists of a Gene Regulatory Engine (GRE) and a Distributed Adaptive Neural Network (DANN). File type identification is one of the phases in computer forensics, especially document file type identification. File type identification is a process of knowing the format of a file to determine the real file type of the file. In this paper, it is shown that DANGLE's performance is better than both EFuNN and ECF in identifying file type. The proposed DANGLE is also capable of identifying document files with an accuracy of 94.33%.