AI-POWERED CYBERSECURITY: MACHINE LEARNING APPROACHES AND THEIR EFFECTIVENESS
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Abstract
The increasing complexity and frequency of cyber threats have necessitated the development of advanced cybersecurity solutions. This review examines the role of artificial intelligence (AI) and machine learning (ML) in enhancing cybersecurity measures. By analyzing various ML approaches, including supervised learning, unsupervised learning, and reinforcement learning, the paper highlights their effectiveness in detecting, preventing, and responding to cyberattacks. The review also discusses key techniques such as anomaly detection, intrusion detection systems, and predictive analytics, evaluating their performance in real-world scenarios. Additionally, the challenges and limitations of implementing AI in cybersecurity are addressed, emphasizing the importance of integrating human expertise with automated systems for optimal results. The findings suggest that AI-powered cybersecurity solutions offer significant advantages in improving threat detection and response times, ultimately contributing to more robust defense mechanisms against evolving cyber threats.
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