Abstract
The rapid evolution of cyber-attacks necessitates advanced security measures. Machine learning (ML), a branch of artificial intelligence (AI), has emerged as a powerful tool for enhancing Cybersecurity. This paper reviews the latest trends in ML for Cybersecurity, including advancements in anomaly detection, adversarial machine learning, automated incident response, federated learning, and explainable AI (XAI). These innovations enable more accurate detection and response to cyber threats. However, the growing integration of ML in Cybersecurity introduces new challenges, such as adversarial attacks on ML models and the need for transparency in AI-driven security solutions.
Published Version
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