Cloud computing has become the backbone of modern digital transformation by providing scalable infrastructure, flexible computing resources, and cost-effective service delivery for enterprises, governments, and individuals. However, the rapid adoption of cloud platforms has also increased the complexity of cybersecurity threats, including malware attacks, ransomware, insider threats, distributed denial-of-service (DDoS) attacks, account hijacking, data breaches, advanced persistent threats (APTs), and zero-day vulnerabilities. Conventional cloud security mechanisms primarily depend on signature-based detection systems and static security policies, which often fail to detect sophisticated and evolving cyberattacks. Recent advancements in Machine Learning (ML), Artificial Intelligence (AI), and cyber threat intelligence have enabled intelligent cloud security systems capable of automatically identifying malicious behavior, predicting emerging threats, and supporting proactive cybersecurity decision-making. This paper presents a comprehensive study on the integration of machine learning and threat intelligence in cloud security, highlighting current trends, major challenges, and future research directions. The proposed framework combines intelligent data collection, feature engineering, machine learning-based anomaly detection, threat intelligence analysis, Explainable Artificial Intelligence (XAI), and blockchain-enabled security management to improve cyber threat detection accuracy and response efficiency. Experimental evaluation demonstrates that AI-assisted threat intelligence significantly enhances attack detection, reduces false alarms, and improves incident response compared with conventional security approaches. Furthermore, Explainable AI improves transparency in security decisions, while blockchain technology ensures secure, tamper-resistant management of security logs and threat intelligence data. The proposed framework provides an intelligent, scalable, and trustworthy solution for protecting cloud computing infrastructures against continuously evolving cyber threats
Keywords : Cloud Security, Machine Learning, Threat Intelligence, Cybersecurity, Intrusion Detection, Anomaly Detection, Explainable Artificial Intelligence, Blockchain, Cloud Computing, Intelligent Security Analytics.
Author : Mr. P. Sathish, Harshit Singal, Appanala Sai Ram Arjun, Kasarla Sai Venkat Sriram, Madikonda Vinay
Title : MACHINE LEARNING AND THREAT INTELLIGENCE IN CLOUD SECURITY: TRENDS, CHALLENGES, AND FUTURE
Volume/Issue : 2026;03(06)
Page No : 1019-1027