Recent Studies and A Review about Detection of Cyber Threats in Cloud Security using Artificial Intelligence

Document Type : Original Article

Authors

1 Cairo

2 Department of Scientific Computing, Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt. The Egyptian School of Data Science (ESDS), Benha, Egypt . Department of Information Systems, College of Information Technology, Misr University for Science and Technology, Giza, Egypt.

3 Computer Science Department, Faculty of Computers and Artificial Intelligence, Benha University, 12311, Egypt

Abstract

Cloud computing has significantly transformed the IT industry through cost-efficient solutions, offering scalable Data. In the cloud, data may be more vulnerable than data on on-site premises. However, its rapid adoption has also introduced new cyber security risks as systems become increasingly vulnerable to sophisticated attacks. Traditional Intrusion Detection Systems (IDS) often face challenges in identifying and mitigating advanced persistent threats, zero-day exploits, and other real-time cyber threats, especially within dynamic cloud environments. This paper analyzes and evaluates the detection of cyber threats in cloud security, focusing on challenges related to recognition, aggregation, and dissemination within user system environments. The research comprehensively review recent studies have leveraged artificial intelligence (AI) methodologies to enhance cyber threat detection. Different deep learning and machine learning approaches are compared based on multiple optimization criteria, including dataset characteristics, simulation environments, real-world deployments, scalability, detection accuracy, coverage of threat types, and overall system performance. Our primary purpose is to offer ideas for the latest progression in cyber-attacks detection in AI, identifying the limitations, open research questions and suggesting potential enhancement for unresolved security challenges.

Keywords

References

References

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