Diverging deep learning cognitive computing techniques into cyber forensics

More than ever before, the world is nowadays experiencing increased cyber-attacks in all areas of our daily lives. This situation has made combating cybercrimes a daily struggle for both individuals and organisations. Furthermore, this struggle has been aggravated by the fact that today's cybercriminals have gone a step ahead and are able to employ complicated cyber-attack techniques. Some of those techniques are minuscule and inconspicuous in nature and often camouflage in the facade of authentic requests and commands. In order to combat this menace, especially after a security incident has happened, cyber security professionals as well as digital forensic investigators are always forced to sift through large and complex pools of data also known as Big Data in an effort to unveil Potential Digital Evidence (PDE) that can be used to support litigations. Gathered PDE can then be used to help investigators arrive at particular conclusions and/or decisions. In the case of cyber forensics, what makes the process even tough for investigators is the fact that Big Data often comes from multiple sources and has different file formats. Forensic investigators often have less time and budget to handle the increased demands when it comes to the analysis of these large amounts of complex data for forensic purposes. It is for this reason that the authors in this paper have realised that Deep Learning (DL), which is a subset of Artificial Intelligence (AI), has very distinct use-cases in the domain of cyber forensics, and even if many people might argue that it's not an unrivalled solution, it can help enhance the fight against cybercrime. This paper therefore proposes a generic framework for diverging DL cognitive computing techniques into Cyber Forensics (CF) hereafter referred to as the DLCF Framework. DL uses some machine learning techniques to solve problems through the use of neural networks that simulate human decision-making. Based on these grounds, DL holds the potential to dramatically change the domain of CF in a variety of ways as well as provide solutions to forensic investigators. Such solutions can range from, reducing bias in forensic investigations to challenging what evidence is considered admissible in a court of law or any civil hearing and many more.