Machine Learning Techniques for Cybersecurity Threat Detection in Cloud Environments

Abstract

The rapid adoption of cloud computing has revolutionized business operations by offering scalability, flexibility, and cost efficiency. However, it has also introduced significant cybersecurity challenges, including sophisticated cyberattacks, insider threats, and vulnerabilities due to misconfigurations. Traditional security measures often fail to address the dynamic and complex nature of cloud environments. This paper explores the application of machine learning (ML) techniques to enhance cybersecurity threat detection in cloud systems. We present a comprehensive framework leveraging supervised, unsupervised, and reinforcement learning algorithms for anomaly detection, malware classification, and threat intelligence. The study includes a literature review of current ML applications, a proposed methodology, implementation details, and testing results using real-world datasets. Our findings demonstrate that ML-driven approaches significantly improve detection accuracy and response times, though challenges such as adversarial attacks and data quality persist. Future research directions include federated learning and explainable AI (XAI) to further enhance cloud security.The rapid adoption of cloud computing has indeed transformed business operations, offering unprecedented scalability, flexibility, and cost efficiency. This paradigm shift has enabled organizations to streamline their processes, reduce infrastructure costs, and rapidly deploy new services. However, the complex and distributed nature of cloud environments has introduced a new set of cybersecurity challenges that traditional security measures struggle to address effectively. These challenges include sophisticated cyberattacks that exploit the interconnected nature of cloud systems, insider threats that leverage privileged access, and vulnerabilities arising from misconfigurations in the complex cloud architecture.

To combat these evolving threats, the application of machine learning (ML) techniques has emerged as a promising approach to enhance cybersecurity threat detection in cloud systems. This paper proposes a comprehensive framework that leverages various ML algorithms, including supervised, unsupervised, and reinforcement learning, to tackle different aspects of cloud security. The framework aims to improve anomaly detection by identifying unusual patterns in network traffic and user behavior, enhance malware classification through advanced feature extraction and analysis, and bolster threat intelligence by correlating data from multiple sources. While the results demonstrate significant improvements in detection accuracy and response times, the study also acknowledges persistent challenges such as adversarial attacks designed to deceive ML models and the critical importance of high-quality, diverse datasets for effective training. Future research directions, including the exploration of federated learning for privacy-preserving collaborative model training and the integration of explainable AI (XAI) techniques to enhance trust and interpretability in ML-driven security decisions, hold promise for further advancing the field of cloud cybersecurity.

Keywords

Machine Learning, Cybersecurity, Cloud Computing, Threat Detection, Anomaly Detection, Malware Classification, Deep Learning, Adversarial Attacks, Explainable AI, Federated Learning

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