Carnegie Mellon University, Software Engineering Institute's Insights (SEI) Blog, last accessed 3 March 2023.
ISSN/ISBN: Not available at this time. DOI: Not available at this time.
Abstract: Detecting anomalous network activity is a powerful way to discover insider threat activities. It is time consuming, however, to establish baseline traffic and process traffic data. This blog post explores how a mathematical law, already used in forensic accounting, may help detect insider activity without the effort of traditional anomaly detection.
Bibtex:
@misc{,
author = {Emily Kessel},
title = {Benford’s law: Potential applications for insider threat detection},
year = {2022},
url = {https://insights.sei.cmu.edu/blog/benfords-law-potential-applications-insider-threat-detection/},
}
Reference Type: Blog
Subject Area(s): Computer Science