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Emerging Paradigms: Leveraging Artificial Intelligence and Machine Learning for Enhanced Wireless Network Security

Ikvinderpal Singh, Sapandeep Kaur Dhillon


Wireless networks have now become an indispensable component of our contemporary communication infrastructure, offering both connectivity and convenience. Nonetheless, the increasing complexity and constantly evolving nature of wireless networks present substantial security challenges. This work investigates the utilization of artificial intelligence (AI) and machine learning (ML) techniques to tackle these security issues in wireless networks. We delve into the principles and practices of applying AI and ML algorithms to enhance security measures, including anomaly detection, intrusion detection and prevention, authentication, and secure data transmission. We also examine the challenges and limitations associated with integrating AI and ML in wireless network security, such as data privacy, adversarial attacks, and resource constraints. By understanding the potential of AI and ML in wireless network security, we can pave the way for more robust and adaptive security mechanisms in the future.


Artificial intelligence, machine learning, wireless networks, security, anomaly detection, intrusion detection and prevention, authentication, data privacy, adversarial attacks, resource constraints

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