Performance Evaluation of Anomaly-Based Detection Approaches for Zero-Day Attack Early Warning in Cloud Infrastructure

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Published: 2026-03-31
Keywords:
zero-day attacks, Cloud infrastructure security, Anomaly detection, Performance evaluation

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Xiaoyi Long
Computer Science, Georgia Institute of Technology, GA, USA

Abstract

The escalating sophistication of zero-day attacks poses unprecedented challenges to cloud infrastructure security, necessitating advanced detection mechanisms beyond traditional signature-based approaches. This paper presents a comprehensive performance evaluation of anomaly-based detection approaches specifically designed for early warning of zero-day attacks in cloud environments. We systematically analyze multiple detection strategies leveraging multi-source telemetry data, including network traffic patterns, system call sequences, and resource usage metrics. Through extensive experimentation on a realistic cloud infrastructure testbed using synthesized attack scenarios, we compare statistical-, machine learning-, and ensemble-based detection approaches across critical performance dimensions, including detection accuracy, false positive rates, and detection timeliness. Our evaluation reveals significant trade-offs among approaches, with ensemble methods achieving a recall (TPR) of 94.7% while maintaining a false positive rate of 0.20%. The findings provide actionable insights for cloud service providers seeking to optimize their zero-day threat detection capabilities.

Article Details

Issue

Vol. 2 No. 1 (2026)

Section

Articles

How to Cite

Performance Evaluation of Anomaly-Based Detection Approaches for Zero-Day Attack Early Warning in Cloud Infrastructure. (2026). Journal of Science, Innovation & Social Impact, 2(1), 341-352. https://sagespress.com/index.php/JSISI/article/view/115

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