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Utilizing Behavior Analysis and Machine Learning to Enhance Defense in Depth
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Soheil Shakeri   , Mehdi Ghahremani |
| Islamic Azad University |
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Abstract: (52 Views) |
Abstract
With the expansion of security threats in cloud computing environments, especially insider threats, the need for innovative solutions to enhance security is increasingly felt. This paper presents a systematic review of the role of User Behavior Analytics (UBA) and Machine Learning (ML) algorithms within the framework of defense in depth. The importance of this topic stems from the fact that traditional methods are unable to identify hidden and internal threats, and the use of intelligent techniques can help in more accurate detection and faster response. Initially, the theoretical foundations of defense in depth and the structures related to behavioral analysis and machine learning are examined. Then, the challenges of implementing these technologies, including resource management, data security, scalability, and high false alarm rates, are analyzed. Subsequently, proposed solutions such as deep learning, federated learning, edge computing, and zero-trust architecture, along with their comparison based on performance criteria, are presented. The overall results of the paper indicate that the integration of reinforcement learning and adaptive user behavior analysis can significantly improve the security of cloud systems. Furthermore, suggestions for future research are provided to enable researchers to overcome current limitations and achieve the design of safer and smarter systems.The main objective of this paper is to investigate how to utilize user behavior analysis and machine learning to enhance defense in depth against cyber threats.
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| Keywords: Machine Learning, Deep Learning, Cybersecurity, Insider Threat Detection, Data Privacy |
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Full-Text [PDF 465 kb]
(48 Downloads)
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Type of Study: Research |
Subject:
Special
Received: 2025/05/18 | Accepted: 2025/09/1 | Published: 2025/09/1
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