Rate My Professor Erza Aminanto

Dr. Erza Aminanto is an Assistant Professor and Program Coordinator in the Master of Cybersecurity program at Monash University Indonesia. He received his Bachelor degree in Electrical Engineering from the Bandung Institute of Technology (ITB), Indonesia, in 2013, followed by a Master degree in the same field from ITB in 2014, and a Ph.D. degree from the School of Computing at the Korea Advanced Institute of Science and Technology (KAIST), South Korea, in 2018. His doctoral thesis focused on deep abstraction and weighted feature selection for Wi-Fi impersonation detection. Previously, he worked as a researcher at the National Institute of Information and Communications Technology (NICT) in Tokyo, Japan, specializing in AI x Security, served as a lecturer at the University of Indonesia (UI) on cyber crime, and currently acts as a Senior Research Data Scientist at Jakarta Smart City while serving on advisory boards for several government and private organizations.

Dr. Aminanto's research expertise spans information security, artificial intelligence, anomaly detection, intrusion detection, cybersecurity, privacy-preserving machine learning, digital transformation, and smart cities, aligning with UN Sustainable Development Goals such as quality education, sustainable cities and communities, and peace, justice, and strong institutions. He leads projects including Enabling Privacy-Preserving Machine Learning for Data-Driven Policy Formulation in Smart City Context and Towards Fair and Trustworthy AI-Enabled Telehealth over Geographically Diverse Regions. His contributions include the Best Paper Award at the International Conference on Information Security and Cryptology (ICISC) 2024 for Does Ordinality in Encoding Matter for XAI? A Case Study on Explainable IDS, which introduces Fusion SHAP to enhance interpretability in intrusion detection systems; Fellowship of the Higher Education Academy (FHEA) in 2025 for excellence in teaching; Best Paper Award at IWBIS 2017; and KAIST Scholarship from 2014 to 2018. Key publications feature Deep Abstraction and Weighted Feature Selection for Wi-Fi Impersonation Detection in IEEE Transactions on Information Forensics and Security, Empowering Digital Resilience: Machine Learning-Based Policing Models for Cyber-Attack Detection in Wi-Fi Networks (Electronics, 2024), and Hack, heist, and havoc: The Lazarus Group’s triple threat to global cybersecurity (Journal of Information Technology Teaching Cases, 2024). He reviews for journals including IEEE Transactions on Information Forensics and Security, Computers & Security, and IEEE Access.