The current landscape of digital forensics employing machine learning approaches: A Review

Abstract

This paper discusses and evaluates the present state of digital forensics, as well as how machine learning techniques are used in this field. The paper covers technological advances in forensics medicine and how we may gain from the performance of machine learning algorithms to compare their performances for improvement on data collection, analysis and investigation. The focus is on the benefits and challenges that may arise while adopting algorithms: Naive Bayes (NB), K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), Principal Component Analysis (PCA) and K-means. Apart from analyzing the latest research and studies in this subject area. Furthermore, tracing new trends in the digital forensics’ domain and outline ways that machine learning can be used for better performance.