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By: Kazi Kutubuddin Sayyad Liyakat, Heena T. Shaikhbn, and Kazi S. S. Liyakat.
1Professor and Head, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India
2Assistant Professor, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India
3Assistant Professor, Department of General Science & Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India
The rapid adoption of cloud computing has revolutionized IT infrastructure, offering unprecedented scalability, flexibility, and cost efficiency. However, this paradigm shift introduces a new spectrum of sophisticated security challenges that traditional, signature-based security mechanisms often struggle to address effectively given the dynamic, multi-tenant, and distributed nature of cloud environments. This paper explores the critical role of Machine Learning (ML) as a potent paradigm for strengthening cloud infrastructure. ML emerges as a vital tool enabling proactive threat detection, intelligent anomaly identification, and automated response capabilities across various cloud layers. We outline how ML algorithms, through the analysis of vast datasets encompassing logs, network traffic, user behavior, and system events, can move beyond reactive defenses to predictive, adaptive, and scalable security postures. Key applications discussed include User and Entity Behavior Analytics, intelligent intrusion detection, real-time malware analysis, vulnerability prediction, and automated compliance verification. The seamless integration of ML promises to significantly bolster resilience against sophisticated, evolving cyber threats, ultimately safeguarding data integrity, confidentiality, and availability in the complex multi-tenant cloud landscape. This approach represents an essential evolution toward building intelligent, autonomous, and robust cloud security systems capable of defending against the next generation of cyberattacks.
Cybersecurity, machine learning, cloud, anomaly detection, user and entity behavior analytics
Citation:
Refrences:
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