A Comprehensive Survey on Computer Forensics: State-of-the-Art, Tools, Techniques, Challenges, and Future Directions

Abstract

With the alarmingly increasing rate of cybercrimes worldwide, there is a dire need to combat cybercrimes timely and effectively. Cyberattacks on computing machines leave certain artifacts on target device storage that can reveal the identity and behavior of cyber-criminals if processed and analyzed intelligently. Forensic agencies and law enforcement departments use several digital forensic toolkits, both commercial and open-source, to examine digital evidence. The proposed research survey focuses on identifying the current state-of-the-art digital forensics concepts in existing research, sheds light on research gaps, presents a detailed introduction of different computer forensic domains and forensic toolkits used for computer forensics in the current era. The proposed survey also presents a comparative analysis based on the tool's characteristics to facilitate investigators in tool selection during the forensics process. Finally, the proposed survey identifies and derives current challenges and future research directions in computer forensics.

A Novel Framework for Mobile Forensics Investigation Process

“…The report relies on solid documentation, photos, notes, and tool-generated content. The examiner should then check the report and edit his con guration if necessary [52] 3.3.3 Results disseminationIt describes the procedure the examiner uses to communicate to policymakers the ndings from the analysis phase. The major goal of this method is to provide action reports for each detected artifact and its analysis.…”

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“…Cyberattacks on electronic media leave certain artefacts in the target device's storage that can detect a cybercriminal and its behaviour if handled and analysed correctly. Law enforcement agencies of the world use several digital forensic tools, both commercial and open source, to achieve digital evidence (Javed et al, 2022).…”

Design and Analysis of Machine Learning Based Technique for Malware Identification and Classification of Portable Document Format Files

“…Image transformation with ML is used for malware identification by the author where the convolution neural network (CNN) is utilized [21]. Similarly, the work in the direction of tools usage and framework representation for the malware analysis has been carried out by the researchers recently [22][23][24][25].…”