Current Trends and Future Directions Exploring Machine Learning Techniques for Cyber Threat Detection
Abstract
The rise of cyber threats has necessitated the development of advanced detection techniques to safeguard sensitive information and infrastructure. This research paper explores current trends and future directions in leveraging machine learning (ML) techniques for cyber threat detection. We examine the efficacy of various ML algorithms, such as supervised learning, unsupervised learning, and reinforcement learning, in identifying and mitigating cyber threats across different domains, including network security, endpoint protection, and application security. The paper provides a comprehensive overview of recent advancements in feature extraction, anomaly detection, and classification methods, emphasizing their practical applications in real-world scenarios. Additionally, we analyze the challenges associated with implementing ML in cybersecurity, including data quality, model interpretability, and the risk of adversarial attacks. By reviewing existing literature and case studies, we highlight emerging trends such as the integration of deep learning and AI-driven automation in threat detection systems. The findings underscore the importance of ongoing research and innovation in machine learning to enhance cyber threat detection capabilities.
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