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United States | Information Technology | Volume 12 Issue 3, March 2023 | Pages: 1874 - 1877
Behavior-Based DDoS Detection for Multi-Vector Attacks in Hybrid Cloud Environments
Abstract: Distributed Denial of Service (DDoS) attacks are becoming increasingly sophisticated, leveraging multi-vector strategies that target vulnerabilities in hybrid cloud environments. Traditional signature-based detection mechanisms struggle to keep pace with evolving attack patterns, necessitating behavior-based approaches that analyze network traffic and system anomalies. This paper presents a behavior-based DDoS detection framework that utilizes machine learning and anomaly detection techniques to identify and mitigate multi-vector attacks in hybrid cloud environments. The proposed solution enhances threat detection accuracy, reduces false positives, and improves response time by leveraging real-time traffic analytics, predictive modeling, and adaptive defense mechanisms. Experimental results demonstrate the efficacy of the framework in identifying complex attack patterns and mitigating their impact on cloud infrastructure.
Keywords: DDoS Detection, Multi-Vector Attacks, Hybrid Cloud Security, Anomaly Detection, Cloud Computing Security
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