IDU-Detector: A Synergistic Framework for Robust Masquerader Attack Detection
Zilin Huang, Xiulai Li, Xinyi Cao, Ke Chen, Longjuan Wang, Logan, Bo-Yee Liu

TL;DR
The paper introduces IDU-Detector, a combined framework using IDS and UEBA for improved detection of masquerader attacks, supported by new diverse datasets, achieving high accuracy in identifying complex attack scenarios.
Contribution
It presents a novel integrated framework for masquerader attack detection and introduces new datasets covering diverse attack vectors to enhance detection capabilities.
Findings
Achieved average detection accuracies of 98.96% and 99.12%.
Effectively detects complex masquerader attacks.
Improves security and response speed.
Abstract
In the digital age, users store personal data in corporate databases, making data security central to enterprise management. Given the extensive attack surface, assets face challenges like weak authentication, vulnerabilities, and malware. Attackers may exploit vulnerabilities to gain unauthorized access, masquerading as legitimate users. Such attacks can lead to privacy breaches, business disruption, financial losses, and reputational damage. Complex attack vectors blur lines between insider and external threats. To address this, we introduce the IDU-Detector, integrating Intrusion Detection Systems (IDS) with User and Entity Behavior Analytics (UEBA). This integration monitors unauthorized access, bridges system gaps, ensures continuous monitoring, and enhances threat identification. Existing insider threat datasets lack depth and coverage of diverse attack vectors. This hinders…
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Taxonomy
TopicsNetwork Security and Intrusion Detection · Advanced Malware Detection Techniques · Anomaly Detection Techniques and Applications
