Distributed autonomous Neuro-Gen Learning Engine for content-based document file type identification

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%.