Smart Digital Forensic Framework for Crime Analysis and Prediction using AutoML

Abstract

Over the most recent couple of years, the greater part of the information, for example books, recordings, pictures, clinical, forensic, criminal and even the hereditary data of people are being pushed toward digitals and cyber-dataspaces. This problem requires sophisticated techniques to deal with the vast amounts of data. We propose a novel solution to the problem of gaining actionable intelligence from the voluminous existing and potential digital forensic data. We have formulated an Automated Learning Framework ontology for Digital Forensic Applications relating to collaborative crime analysis and prediction. The minimum viable ontology we formulated by studying the existing literature and applications of Machine learning has been used to devise an Automated Machine Learning implementation to be quantitatively and qualitatively studied in its capabilities to aid intelligence practices of Digital Forensic Investigation agencies in representing, reasoning and forming actionable insights from the vast and varied collected real world data. A testing implementation of the framework is made to assess performance of our proposed generalized Smart Forensic Framework for Digital Forensics applications by comparison with existing solutions on quantitative and qualitative metrics and assessments. We will use the insights and performance metrics derived from our research to motivate forensic intelligence agencies to exploit the features and capabilities provided by AutoML Smart Forensic Framework applications.