AI AND MACHINE LEARNING FOR NETWORK SECURITY: APPLICATIONS AND CASE STUDIES

Yamini Kannan

Authors

Yamini Kannan New York, United States. Author

Keywords:

Artificial Intelligence (AI), Machine Learning (ML), Network Security, Intrusion Detection Systems (IDS), Phishing Detection, Malware Analysis, Cybersecurity

Abstract

In today's interconnected world, network security has become a paramount concern for organizations across all sectors due to the increasing sophistication of cyber threats such as malware, phishing attacks, and advanced persistent threats (APTs). Traditional security mechanisms are often inadequate in the face of rapidly evolving threat landscapes, necessitating the integration of Artificial Intelligence (AI) and Machine Learning (ML) into network security strategies. AI and ML offer promising solutions by leveraging vast amounts of data to detect and mitigate network threats in real-time, enhancing the capabilities of traditional security systems. This paper reviews the application of AI and ML in detecting and mitigating network threats, exploring fundamental concepts, benefits, challenges, and presenting case studies that demonstrate successful deployments of AI/ML in cybersecurity. Through this analysis, the transformative potential of AI/ML technologies in safeguarding digital infrastructures is highlighted, along with future research directions and potential advancements in this field.

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