AI-DRIVEN THREAT DETECTION IN DISTRIBUTED CLOUD SYSTEMS
DOI:
https://doi.org/10.29121/shodhkosh.v4.i2.2023.3359Keywords:
AI-Driven Security, Threat Detection, Distributed Cloud Systems, Machine Learning, Deep Learning, Advanced Persistent Threats, Federated LearningAbstract [English]
Distributed cloud systems are getting more complicated, which means we need more advanced ways to find threats. AI-driven methods use machine learning and deep learning to find, predict, and stop cyber risks with a level of accuracy and speed that has never been seen before. This paper looks into how artificial intelligence can help protect distributed cloud infrastructures. It focusses on how AI can be used for threat intelligence, anomaly detection, and automated reaction. Different methods, like neural networks, natural language processing, and collaborative learning, are tested to see how well they can find complex attacks like Advanced Persistent Threats (APTs) and Distributed Denial of Service (DDoS) attacks. The study also talks about the problems that come up when you try to use AI to find threats, like uneven data, the need for a lot of computing power, and models that are hard to understand. Real-life examples show how AI is used in a wide range of fields, highlighting its transformative promise in cloud security. Future trends are looked at, such as quantum AI and security operations centres (SOC) that use AI. This research shows how AI technologies are changing the way threat monitoring works in distributed cloud systems, making them more resistant to new cyber threats.
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Copyright (c) 2023 Ravindrakumar
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