Abstract
As cyber threats continue to evolve in complexity and sophistication, traditional methods of network security are often unable to keep pace. The integration of Artificial Intelligence (AI) and Machine Learning (ML) offers significant advancements in defending networks against these growing threats. This paper explores the transformative role of AI and ML in network security, focusing on how these technologies enhance threat detection, prevention, and response. By analyzing their capabilities in anomaly detection, predictive analytics, and automated responses, the paper highlights the future potential of AI and ML in creating adaptive, self-learning cybersecurity systems. Furthermore, the challenges and ethical considerations surrounding the use of AI in network defense, including biases in algorithms and potential misuse, are discussed. Through case studies and a detailed analysis of current implementations, the paper aims to offer insights into how organizations can leverage these technologies to create a robust defense mechanism against evolving cyber threats.
Keywords: Anomaly detection, Artificial intelligence, Automated response, Cyber defense, Cyber threats, Machine learning, Network security, Predictive analytics, Self-learning systems, Threat detection.
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