Automated strategies for collaborative investigations of digital logs

Recent advancements in the size, framework, contextual and conceptual understanding of business data across the globe have subjected digital forensics to strict criticisms from numerous angles. Thus, the efficacies of most scientifically proven methods for identifying, collecting, investigating, analysing, interpreting, validating, documenting, reporting and preserving digital evidence rapidly become ineffective to support collaborative digital forensic purposes. Hence, this paper examines some of these core issues and further proposes Log-splitter that can be used to minimize them. In addition, C++ programming language is used to implement the model on the platform of Windows Operating System. The model is subsequently evaluated with series of datasets. The results obtained suggest that Log-splitter can automatically split digital evidence and assigned them to different investigators that are defined by the end-users to quicken the conclusion of digital cases. Furthermore, the results illustrate that investigations of some digital evidence can demonstrate at least three fundamental concepts. Above all, good implementation is the best strategy to mitigate the possibility of biasness of the Log-splitting processes towards one investigator than the other investigators.