AI for Wireless Network Protection

The number of wireless devices and the rising volume of data transmitted over Wi-Fi, Bluetooth, and 5G networks are inevitably increasing the risk of a cyber incident. This chapter examines the potential of utilizing artificial intelligence (AI) to detect and analyze cyber threats in wireless networks effectively. The analysis of modern approaches to cybersecurity, particularly traditional methods such as signature analysis and anomaly detection, is conducted, and their significant limitations in countering the latest attacks are described. A methodology is developed for integrating large AI language models (GPT-4.1, Gemini 1.5 Flash, and Claude Sonnet 4) into a multi-model system to analyze real-time router logs. The proposed system architecture ensures effective interaction of AI algorithms for the rapid identification of various types of attacks. An experimental study involving real network attacks demonstrated high efficiency of the approach, with the Claude Sonnet 4 model achieving 100% accuracy. At the same time, the multi-model configuration demonstrated the optimal balance between accuracy and minimizing false positives. Based on the study results, practical recommendations are provided for implementing such intelligent cybersecurity systems under real conditions, particularly relevant for organizations with different needs and resources. Promising areas for future research are also outlined, particularly the integration of the proposed system with cloud services and federated learning.