Reduction of digital forensic evidence using data science

Abstract

The hasty headway in the field of information technology has lead ways for an escalating crime rate being technically exhaustive. The crimes involving digital tools and devices assist to be the forensic evidences. An upsurge in digital evidences is coalesced with the growing size of storage devices. Pertaining to the ineffectualness of the traditional analysis methods to handle the colossal amount of digital data, the forensic investigators have to adopt big data analytics to store, recover, and analyze the digital evidence. The storage of digital evidence calls for surveillance and security, thereby preserving its evidential significance. The digital analysis and fraud detection make the recovery and storage of digital data achievable by effective data reduction and exploiting the features of data mining for storage and data archive. Advancement with the forensic analysis assures automated management of digital data thus safeguarding the sensitivity of data. The paper aims to take the facets of data reduction for efficient storage and retrieval of digital data, and an overall digital forensic research framework has been outlined. The proposed work supports the existing framework for data reduction and storage. It also outlines the challenges and the unaddressed aspects of digital forensics. In this paper, I also discussed the unaddressed aspects of forensic investigations and peaks into the loopholes and the opportunity realms that can lay groundwork for future.