A proactive malicious software identification approach for digital forensic examiners

Ali, Muhammad, Shiaeles, Stavros, Clarke, Nathan, Kontogeorgis, Dimitrios

arXiv.org Artificial Intelligence 

Digital investigators often get involved with cases which seemingly point the responsibility to the person to which the computer belongs, but after a thorough examination malware is proven to be the cause, causing loss of precious time. Whilst Anti-Virus (AV) software can assist the investigator in identifying the presence of malware, with the increase in zero-day attacks and errors that exist in AV tools, this is something that cannot be relied upon. The aim of this paper is to investigate the behavior of malware upon various Windows operating system versions in order to determine and correlate the relationship between malicious software and OS artifacts. This will enable an investigator to be more efficient in identifying the presence of new malware and provide a starting point for further investigation. The study analyzed several versions of the Windows operating systems (Windows 7, 8.1 and 10) and monitored the interaction of 90 samples of malware across three categories of the most prevalent (Trojan, Worm, and Bot) and 90 benign samples through the Windows Registry.