Journal Articles (Published)

[J.1] Arnob, A. K. B., Mridha, M. F., Safran, M., Amiruzzaman, M., & Islam, M. R. (2025). An enhanced LSTM approach for detecting IoT-based DDoS attacks using honeypot data. International Journal of Computational Intelligence Systems, 18(1), 1–22. https://doi.org/10.1007/s44196-025-00741-7
(Scopus Q2)

[J.2] Arnob, A. K. B., Chowdhury, R. R., Chaiti, N. A., Saha, S., & Roy, A. (2025). A comprehensive systematic review of intrusion detection systems: Emerging techniques, challenges, and future research directions. Journal of Edge Computing (Early Access). https://doi.org/10.55056/jec.885
(Scopus-indexed)

[J.3] Arnob, A. K. B., & Jony, A. I. (2024). Comparing machine learning algorithms for breast cancer diagnosis: Wisconsin diagnostic dataset analysis. International Journal of Data Science and Big Data Analytics, 4(2), 1–11. https://doi.org/10.51483/IJDSBDA.4.2.2024.1-11

[J.4] Arnob, A. K. B., & Jony, A. I. (2024). Enhancing IoT security: A deep learning approach with feedforward neural network for detecting cyber attacks in IoT. Malaysian Journal of Science and Advanced Technology, 4(4), 413–420. https://doi.org/10.56532/mjsat.v4i4.299

[J.5] Jony, A. I., & Arnob, A. K. B. (2024). A long short-term memory based approach for detecting cyber attacks in IoT using CIC-IoT2023 dataset. Journal of Edge Computing, 3(1), 28–42. https://doi.org/10.55056/jec.648
(Scopus-indexed)

[J.6] Jony, A. I., & Arnob, A. K. B. (2024). Deep learning paradigms for breast cancer diagnosis: A comparative study on Wisconsin diagnostic dataset. Malaysian Journal of Science and Advanced Technology, 4(2), 109–117. https://doi.org/10.56532/mjsat.v4i2.245

[J.7] Jony, A. I., & Arnob, A. K. B. (2024). Securing the Internet of Things: Evaluating machine learning algorithms for detecting IoT cyberattacks using CIC-IoT2023 dataset. International Journal of Information Technology and Computer Science, 16(4), 56–65. https://doi.org/10.5815/ijitcs.2024.04.04
(Scopus Q3)

Conference Papers

[C.1] Arnob, A. K. B., Naim, M. A., Rezwan, T., & Hasan, M. M. (2024). Utilizing kidney ontology for data-driven exploration of potential biomarkers in kidney diseases: Introducing the Kidney Diseases Biomarker Ontology (KDBO). Proceedings of the 3rd International Conference on Computing Advancements (ICCA 2024) (pp. 1–8). ACM. https://doi.org/10.1145/3723178.3723230