Published 13-06-2024
Keywords
- anomaly detection,
- cloud networks,
- machine learning
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Abstract
The proliferation of cloud computing has necessitated robust and intelligent mechanisms for securing network infrastructures against anomalies and potential intrusions. Traditional rule-based systems often lack the adaptability required to detect novel or evolving threats in dynamic cloud environments. This paper investigates the application of advanced machine learning algorithms for anomaly detection in cloud networks, emphasizing supervised, unsupervised, and hybrid approaches. A comprehensive analysis of algorithmic performance, including Random Forest, Support Vector Machines, Autoencoders, and Deep Neural Networks, is presented with respect to accuracy, false positive rates, and computational overhead. The study also explores real-time anomaly detection using streaming data and the integration of cloud-native monitoring tools. Findings suggest that machine learning-driven models, when properly trained and continuously updated, significantly enhance the detection of both known and zero-day anomalies, ensuring resilient and adaptive cloud security frameworks.
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