AI-Driven Threat Detection and Response: Toward Autonomous Cyber Defense Systems

The increasing sophistication of cyber threats in modern digital infrastructures necessitates intelligent, autonomous defense mechanisms capable of responding faster and more accurately than humans. This study introduces an AI-Driven Threat Detection and Response (AI-TDR) framework that integrates deep learning and reinforcement learning to autonomously detect, analyze, and mitigate cyberattacks in real time. Using the UNSW-NB15 dataset, which contains realistic traffic and nine attack types, three architectures, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Transformer, were developed and tested. The CNN and LSTM achieved 100% accuracy, while the Transformer reached 96.8% accuracy with an AUC of 0.996, demonstrating robustness and generalization. The AI-TDR operates through a Perception–Cognition–Decision–Action cycle, enabling adaptive learning and autonomous mitigation through continuous feedback. By combining spatial, temporal, and contextual intelligence, the system advances toward self-learning, multi-agent cyber defense. Beyond detection, it envisions automated responses such as node isolation and firewall reconfiguration. Future work includes integrating Explainable AI for transparency, adversarial training for resilience, and federated learning for decentralized protection. Overall, this research contributes to the advancement of adaptive and intelligent cybersecurity, supporting global efforts to achieve continuous and collaborative defense in an evolving threat landscape.