AI-POWERED CLOUD SECURITY: A REVIEW OF INTRUSION DETECTION AND PREVENTION STRATEGIES
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Abstract
Cloud computing has been able to revolutionize current organizations through the usefulness of scaling, flexibility, and affordability of data storage and access. However, a number of serious security problems have been brought about by the widespread use of cloud services, including data breaches, malware insertion, denial-of-service (DoS), and unauthorized access. Although they can offer much-needed protection, traditional techniques like firewalls, intrusion detection systems (IDS), intrusion prevention systems (IPS), and log inspection are ineffective in addressing the new dangers of today. Cloud security systems are increasingly being implemented using Artificial Intelligence (AI) and Machine Learning (ML) to help with anomaly detection, predictive threat modelling, and automated incident response in order to get around these limitations. The paper provides a survey of AI-driven solutions that can be used to improve cloud security, paying attention to the next-generation intrusion detection and prevention models that include Web Intrusion Detection Systems (WIDS), host-based and network-based IDPS, and IoT-related IPS. It also examines standards of cloud security, mitigation methods and problems of multi-tenant and hybrid environments. The major gaps, such as data privacy issues, false positives, adversarial attacks, and the complexity of integration, are examined. Lastly, the paper examines how AI can be integrated with new technologies like blockchain, edge computing, and IoT to construct flexible, adaptable, and resilient cloud security environments
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