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, Assistant Professor, Faculty of Business and Information Technology, and recognized as , has published several influential papers in prestigious journals and conferences on AI-driven automation for 6G networks and cybersecurity:

1. : Published in IEEE Transactions on Communications (IF: 7.2), this paper introduces an automated security framework integrating Physical Layer Authentication and Cross-Layer Intrusion Detection Systems to tackle security challenges across multiple Internet protocol layers. The framework employs drift-adaptive online learning and an enhanced Successive Halving-based Automated Machine Learning (AutoML) method to dynamically generate optimized machine learning models for evolving network environments. Implementation code is available on .
2. : Published and received the at the International Workshop on Autonomous Cybersecurity (AutonomousCyber 2024), held in conjunction with the 31st Association for Computing Machinery Conference on Computer and Communications Security (2024), one of the top-3 cybersecurity conferences globally. This paper presents an AutoML-based autonomous intrusion detection system that fully automates all key processes in cybersecurity data analytics, including data pre-processing, feature engineering, model selection, hyperparameter tuning, and model ensemble. Implementation code is available on .
3. : Published in IEEE Transactions on Network and Service Management (IF: 4.7), this paper provides a comprehensive review of modern network security challenges and explores how AutoML can develop resilient security solutions for current and future networks. It also presents practical case studies on AutoML-powered intrusion detection systems and strategies for defending against Adversarial Machine Learning attacks. A tutorial with implementation code is available on .
4. : Published in Computer Networks (IF: 4.4), this paper provides an in-depth survey on Zero-Touch Network frameworks, covering aspects such as network optimization, traffic monitoring, energy efficiency, and security automation. The study highlights the potential of Zero-Touch Service Management and AutoML in transforming 5G+ and next-generation network management.

Overall, Dr. Yang and his team’s research addresses critical, real-world challenges in modern networking and cybersecurity, making significant strides toward fully autonomous cybersecurity solutions for next-generation networks. His work has been widely recognized by the cybersecurity and networking research communities and has the potential to revolutionize cybersecurity applications. His publications and open-source code contributions on autonomous AI and cybersecurity have received over 4,000 citations and 3,000 stars, demonstrating their impact and practical value.