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About Thesis Hakonen Petri - Detecting Insider Threats
This document is a master's thesis that examines detecting insider threats using user and entity behavior analytics (UEBA). The thesis researches insider threat taxonomy, common behavioral indicators, how those indicators could be detected in technical logs using UEBA tools, and prioritized use cases. The research found that traditional security methods are still important but more effective when combined with AI-driven UEBA. UEBA tools take a different approach to anomaly detection using various analytics methods like basic analytics (e.g. statistics, patterns) and advanced analytics relying on AI. The thesis also details several key insider threat use cases that could be detected using UEBA, such as account misuse, compromised accounts, data exfiltration, and
Detailed Information
| Author: | ['alexandreppinheiro'] |
|---|---|
| Publication Year: | 2023 |
| Pages: | 72 |
| Language: | English |
| Format: | |
| Price: | FREE |
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