An Enhanced Multiclass Support Vector Machine Model and its Application to Classifying File Systems Affected by a Digital Crime

Abstract The digital revolution we are witnessing nowadays goes hand in hand with a revolution in cybercrime. This irrefutable fact has been a major reason for making digital forensic (DF) a pressing and timely topic to investigate. Thanks to the file system which is a rich source of digital evidence that may prove or deny a digital crime. Yet, although there are many tools that can be used to extract potentially conclusive evidence from the file system, there is still a need to develop effective techniques for evaluating the extracted evidence and link it directly to a digital crime. Machine learning can be posed as a possible solution looming in the horizon. This article proposes an Enhanced Multiclass Support Vector Machine (EMSVM) model that aims to improve the classification performance. The EMSVM suggests a new technique in selecting the most effective set of parameters when building a SVM model. In addition, since the DF is considered a multiclass classification problem duo to the fact that a file system might be accecced by more than one application, the EMSVM enhances the class assignment mechanism by supporting multi-class classification. The article then investigates the applicability of the proposed model in analysing incriminating digital evidence by inspecting the historical activities of file systems to realize if a malicious program manipulated them. The results obtained from the proposed model were promising when compared to several machine-learning algorithms.