Faculty of Science and Engineering
Department of Electrical and Electronic Engineering
B.Sc. in Electrical and Electronics Engineering (RUET)
Najmul Alam received his Bachelor of Science (B.Sc.) degree in Electrical and Electronic Engineering from Rajshahi University of Engineering and Technology (RUET), Rajshahi, Bangladesh, in 2024. He was the recipient of a technical scholarship throughout his academic years from 2019 to 2024. His research interests encompass cybersecurity, cyber-physical security, machine learning, and forecasting.
Lecturer
Dept. of Electrical and Electronic Engineering
City University
From September 26, 2025, to the present
Machine Learning; Cyber Security; Adversarial Learning; Cyber Physical Security; Electric Vehicle
[1] N. Alam, M. A. Rahman, Md. A. Hossain, and Md. R. Islam, Secure electric vehicle charging station demand forecasting under adversarial and false data injection attacks, Engineering Science and Technology, an International Journal, vol. 71, p. 102203, 2025. doi: 10.1016/j.jestch.2025.102203.
[2] N. Alam, Md. A. Hossain, Md. F. Ishraque, M. A. Rahman, and Md. R. Islam, ”Cyber resilient machine learning models for estimating synchronous machine excitation current: Mitigating the impact of DoS attacks,” International Journal of Electrical Power & Energy Systems, vol. 171, p. 111055, 2025. doi: 10.1016/j.ijepes.2025.111055.
[3] N. Alam, M. A. Rahman, M. R. Islam, and M. J. Hossain, ”Ensemble adversarial training-based robust model for multi-horizon dynamic line rating forecasting against adversarial attacks,” Electric Power Systems Research, vol. 241, p. 111289, 2025. doi: 10.1016/j.epsr.2024.111289.
[4] N. Alam, M. A. Rahman, M. R. Islam, M. A. Hossain, M. A. H. Sadi, and M. J. Hossain, ”Robust energy forecasting in combined cycle power plants: mitigating cyberattacks on machine learning models,” IET Generation, Transmission & Distribution, vol. 20, no. 1, p. e70216, 2026. doi: 10.1049/gtd2.70216.
[5] N. Alam, M. A. Rahman and M. R. Islam, DynaLiRD: A dataset for dynamic line rating of overhead transmission lines, utilizing meteorological data and grid parameters based on the IEEE 738-2012 standard, Data in Brief, 2025, p. 112065. doi: 10.1016/j.dib.2025.112065.
[6] N. Alam, M. A. Rahman, M. R. Islam, and M. J. Hossain, ”Machine learning-based multivariate forecasting of electric vehicle charging station demand,” Electronics Letters, vol. 60, p. e70104, 2024. doi: 10.1049/ell2.70104.