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Muhammad Johan Alibasa

Assistant Professor, Cybersecurity

Accepting PhD students

Johan is currently an Assistant Professor in the Master of Cybersecurity program at Monash University, Indonesia. He earned his Ph.D. from the School of Computer Science at The University of Sydney, Australia, where he was honoured with the School of Computer Science Research Students Excellence Prize.

His research interests include human-computer interaction, software engineering, and applications of AI. Additionally, he explores affective computing and investigates how digital technology use relates to well-being. His work also focuses on automating secure software engineering with AI-powered code analysis and designing human-cantered authentication systems that balance security and usability.

Before joining Monash University, Johan was a lecturer at Telkom University and Sampoerna University in Indonesia. At these institutions, he played a key role in developing the curriculum for the Bachelor of Software Engineering and Bachelor of Information Systems programs, respectively. He is also actively involved in several international conferences as a committee member. Johan holds both a B.Sc. and an M.Sc. in Electrical Engineering from Institut Teknologi Bandung, Indonesia, which he received in 2012 and 2014, respectively.

More information

Zhang, Y., Suleiman, B., Alibasa, M. J., & Farid, F. (2024). Privacy-aware anomaly detection in iot environments using fedgroup: A group-based federated learning approach. Journal of Network and Systems Management, 32(1), 20.
Alibasa, M. J., Calvo, R. A., & Yacef, K. (2023). Predicting mood from digital footprints using frequent sequential context patterns features. International Journal of Human–Computer Interaction, 39(10), 2061-2075.
Alibasa, M. J., Purwanto, R. W., Yacef, K., Glozier, N., & Calvo, R. A. (2020). Doing and feeling: relationships between moods, productivity and task-switching. IEEE Transactions on Affective Computing, 13(3), 1140-1154.
Alibasa, M. J., Calvo, R. A., & Yacef, K. (2019). Sequential pattern mining suggests wellbeing supportive behaviors. IEEE Access, 7, 130133-130143.
Osop, H., Suleiman, B., Alibasa, M. J., Wrigley, D., Helsham, A., & Asmaro, A. (2023, June). Improving Patients’ Length of Stay Prediction Using Clinical and Demographics Features Enrichment. In International Conference on Computational Science (pp. 120-128). Cham: Springer Nature Switzerland.
Alibasa, M. J., Suleiman, B., Bello, A., Anaissi, A., Yan, Q., & Chen, S. (2023, May). Cloud Resources Usage Prediction Using Deep Learning Models. In International Conference on Advances in Computing Research (pp. 443-452). Cham: Springer Nature Switzerland.
Zhang, Y., Suleiman, B., & Alibasa, M. J. (2022, November). Fedgroup: a federated learning approach for anomaly detection in iot environments. In International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services (pp. 121-132). Cham: Springer Nature Switzerland.
Leung, C. Y. C., Suleiman, B., Alibasa, M. J., & Al-Naymat, G. (2022, November). Anonymous Yet Alike: A Privacy-Preserving DeepProfile Clustering for Mobile Usage Patterns. In International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services (pp. 81-100). Cham: Springer Nature Switzerland.
Suleiman, B., Anaissi, A., Alibasa, M. J., & Truong, H. (2022, June). Multi-contextual recommender using 3D latent factor models and online tensor decomposition. In International conference on computational science (pp. 276-290). Cham: Springer International Publishing.
Zhao, J., Suleiman, B., & Alibasa, M. J. (2022, November). Feature encoding by location-enhanced word2vec embedding for human activity recognition in smart homes. In International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services (pp. 191-202). Cham: Springer Nature Switzerland.
Suleiman, B., Lu, K., Chan, H. W., & Alibasa, M. J. (2021, November). DeepPatterns: Predicting Mobile Apps Usage from Spatio-Temporal and Contextual Features. In International Conference on Service-Oriented Computing (pp. 811-818). Cham: Springer International Publishing.
Alibasa, M. J., & Calvo, R. A. (2019, September). Supporting mood introspection from digital footprints. In 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII) (pp. 96-101). IEEE.
Alibasa, M. J., Santos, M. R., Glozier, N., Harvey, S. B., & Calvo, R. A. (2017, December). Designing a secure architecture for m-health applications. In 2017 IEEE Life Sciences Conference (LSC) (pp. 91-94). IEEE.