Dr. Fei Hu is a professor in the department of Electrical and Computer Engineering at the University of Alabama. He has published over 10 technical books with CRC press. His research focus includes cyber security and networking. He obtained his Ph.D. degrees at Tongji University (Shanghai, China) in the field of Signal Processing (in 1999), and at Clarkson University (New York, USA) in Electrical and Computer Engineering (in 2002). He has published over 200 journal/conference papers and books. Dr. Hu's research has been supported by U.S. National Science Foundation, Cisco, Sprint, and other sources. He won the school's President's Faculty Research Award (

Part I. Secure AI/ML Systems: Attack Models

1. Machine Learning Attack Models, 2. Adversarial Machine Learning: A New Threat Paradigm for Next-generation Wireless Communications,3. Threat of Adversarial Attacks to Deep Learning: A Survey,4. Attack Models for Collaborative Deep Learning,5. Attacks on Deep Reinforcement Learning Systems: A Tutorial,6. Trust and Security of Deep Reinforcement Learning,7. IoT Threat Modeling using Bayesian Networks

Part II. Secure AI/ML Systems: Defenses

8. Survey of Machine Learning Defense Strategies,9. Defenses Against Deep Learning Attacks,10. Defensive Schemes for Cyber Security of Deep Reinforcement Learning, 11. Adversarial Attacks on Machine Learning Models in Cyber-Physical Systems,12. Federated Learning and Blockchain: An Opportunity for Artificial Intelligence with Data Regulation

Part III. Using AI/ML Algorithms for Cyber Security

13. Using Machine Learning for Cyber Security: Overview,14. Performance of Machine Learning and Big Data Analytics Paradigms in Cyber Security,15. Using ML and DL Algorithms for Intrusion Detection in Industrial Internet of Things.

Part IV. Applications

16. On Detecting Interest Flooding Attacks in Named Data Networking (NDN)-based IoT Searches, 17. Attack on Fraud Detection Systems in Online Banking Using Generative Adversarial Networks,18. An Artificial Intelligence-assisted Security Analysis of Smart Healthcare Systems,19. A User-centric Focus for Detecting Phishing Emails