Forensic data acquisition, storage, and analysis pose incredible software and hardware challenges. The development of efficient algorithms for acquiring hundreds of gigabytes and terabytes of digital incident evidence is necessary. The conversion of sequential digital forensic data analysis algorithms for distributed or parallel processing is extremely intricate.

Methods for forensic data collection, duplication, and analysis exist in the literature [1]. However, the need exists to develop smarter tools for the live capturing and extraction of relevant evidence from gigantic targets. In efforts to bolster superior evidence acquisition and analysis tools, the authors discuss the drawbacks of current digital forensics tools and computer systems. Indisputably, future digital forensics tools should support fault tolerance and parallel inquiries of forensics operations on distributed and parallel computers. The authors persuasively illuminate the cumbersome manual reviews of image thumbnails, and the scantiness of recent automatic forensic image recognition and reconstruction algorithms. Accordingly, they put forward gripping evidence for the need to develop intelligent digital forensics analysis tools. An intelligent digital evidence analysis tool ought to support reliable, automatic forensic image processing and analysis in a distributed or parallel environment.