Digital Forensic Analysis of Files Using Deep Learning
Mohammed Al Neaimi, Hussam Al Hamadi, Chan Yeob Yeun, Mohamed Jamal Zemerly
Abstract
Digital forensic experts are responsible for assisting law enforcement in extracting evidence from electronic devices. Identifying a file type within digital evidence is an essential part of the forensic practice. This paper investigated the existing forensic approaches to identify the file type and developed a new approach based on deep learning and overcome previous approaches' limitations. This paper also highlighted the difference between modern and traditional methods to conduct such an analysis. Whereas, most traditional techniques have been identified to have challenges emanating from the approach structure, which influences how file types are identified, which has prompted researchers in the field to look for new systems that will address this gap. Thus, a new methodology is proposed, which will utilize deep learning techniques to provide a model able to predict corrupted files.