AI-driven solutions for proactive network security and threat detection

This paper introduces the Adaptive Threat Detection Network (ATDN) designed to address the complex and diverse cyber threats in university networks. ATDN leverages reinforcement learning, multi-modal data fusion, and adversarial training to improve the detection of both known and unknown threats. We evaluated ATDN using three open-source datasets—NSL-KDD, CICIDS2017, and UNSW-NB15—and compared its performance against several baseline algorithms. The results show that ATDN significantly outperforms traditional models in terms of accuracy, precision, recall, and F1-Score. The ablation study highlights the key roles of reinforcement learning and multi-modal data fusion in the model’s performance. ATDN efficiently handles the complex traffic patterns in university networks, maintaining low latency for real-time response. Future research will focus on extending ATDN to handle encrypted traffic and incorporating additional data sources to further strengthen its ability to mitigate modern network threats.