法医学研究ジャーナル

Exploring the Efficacy of a Novel Support Vector Neural Network for Anomaly Detection in Digital Forensics Data

Abstract

Sofia Hana

Anomaly detection in digital forensics data is critical for identifying suspicious activities and potential security breaches. This mini-review investigates the efficacy of a novel Support Vector Neural Network (SVNN) for anomaly detection in digital forensics datasets. By examining recent literature, this article elucidates the principles of SVNN, its advantages over traditional methods, and its application in detecting anomalous behavior in various forensic scenarios. Furthermore, it discusses challenges, opportunities, and future directions for enhancing anomaly detection using SVNN in digital forensics investigations.