Next-Generation Cyber Defense: Transformer-Based AI for Threat Detection and Autonomous Response in Dynamic Environments

The escalating sophistication of cyber threats in dynamic digital ecosystems demands the deployment of intelligent and adaptive defense mechanisms capable of real-time detection, interpretation, and mitigation. This research presents a Transformer-Based Threat Detection and Response Framework, an advanced AI-driven cybersecurity architecture designed to autonomously identify and counter malicious activities in large-scale networked environments. Utilizing the comprehensive UNSW-NB15 dataset, the proposed model adopts a customized encoder-only Transformer architecture that effectively captures long-range contextual dependencies among network traffic features such as packet rates, flow duration, and protocol behavior. The model incorporates multi-head self-attention, layer normalization, and global pooling to extract discriminative representations crucial for attack identification, while a reinforcement learning module enables adaptive and context-aware response selection. Experimental results demonstrate an impressive 96.78% classification accuracy, underscoring the framework’s superior performance and generalization across multiple attack vectors, including Denial of Service (DoS), reconnaissance, and infiltration attempts. Comparative evaluations confirm that the attention-based mechanism enhances sensitivity to subtle threat patterns that traditional sequential and convolutional models often overlook. Furthermore, attention-weight visualization contributes to interpretability and transparency, supporting human trust and explainable AI in cybersecurity. Overall, this study establishes the viability of Transformer-based architectures as a cornerstone for next-generation, autonomous, and interpretable cyber defense systems applicable to cloud, IoT, and industrial control environments.