Prof. Sharmila S P | Computer Engineering | Editorial Board Member

Published on by Engineering Scientist

Prof. Sharmila S P | Computer Engineering
| Editorial Board Member

Siddaganga Institute of Technology Tumakuru | India

Prof. Sharmila S P the research work focuses on advancing cybersecurity through AI-driven, explainable, and resilient detection mechanisms capable of addressing modern, highly obfuscated threats. Central contributions include the development of memory-forensic-based feature extraction techniques that enhance the transparency and interpretability of obfuscated malware detection models, enabling isolated family distinction and reducing false positives. The work explores multi-class classification frameworks for malware analysis, leveraging machine learning paradigms to identify sophisticated adversarial behaviors across diverse threat categories. Additional research investigates Hidden Markov Model–based intrusion detection, employing a randomized Viterbi algorithm to strengthen anomaly recognition in dynamic network environments. Studies on cyber-attack prediction further analyze prevalent forecasting techniques to improve proactive defense capabilities. Complementary research examines Android malware behavior, distributed ledger applications for secure banking operations, and lightweight authentication mechanisms rooted in keystroke dynamics for user verification. With a strong emphasis on AI, machine learning, GNNs, NLP-driven analysis, reverse engineering, and volatile memory forensics, the overall body of work contributes toward building robust, explainable, and scalable cybersecurity systems capable of safeguarding digital infrastructures against evolving threats in cloud environments, embedded systems, mobile platforms, and large-scale networked ecosystems.

 Profile:  Orcid 

Featured Publications

Sharmila, S. P., Gupta, S., Tiwari, A., & Chaudhari, N. S. (2025). Unveiling evasive portable documents with explainable Kolmogorov–Arnold networks resilient to generative adversarial attacks. Applied Soft Computing, 138, 113537. https://doi.org/10.1016/j.asoc.2025.113537

Sharmila, S. P., Gupta, S., Tiwari, A., & Chaudhari, N. S. (2025). Leveraging memory forensic features for explainable obfuscated malware detection with isolated family distinction paradigm. Computers and Electrical Engineering, 121, 110107. https://doi.org/10.1016/j.compeleceng.2025.110107

Joel Freidy Ebolembang | Complex Systems | Best Researcher Award

Published on by Engineering Scientist

Mr. Joel Freidy Ebolembang | Complex Systems
| Best Researcher Award

Mr. Joel Freidy Ebolembang | National Higher Polytechnic School of Douala | Cameroon

Mr. Joel Freidy Ebolembang is a dynamic PhD candidate in his third year at the Energy, Materials, Modeling, and Methods Laboratory of the National Higher Polytechnic School of Douala, Cameroon, where his research focuses on the innovative use of artificial intelligence and simulation for the control and diagnostics of dual-fuel engines. Holding a Master’s degree in Energy Research and an Engineering degree in Mechanical Construction, he has developed strong expertise in modeling, deep learning, and energy optimization, positioning himself at the intersection of mechanics, energy, and artificial intelligence. His academic journey has been enriched with advanced projects, including the study of diesel engine performance and the modeling of fatigue wear in automotive brake pads. He has published in reputable journals such as International Journal of Heat and Technology and Applied Sciences, and is actively working on novel applications of neural networks for intelligent diagnostics of diesel engines. Alongside his research, he has practical professional experience as an Assistant Train Driver at Camrail since 2020, where he ensures the safe and efficient operation of critical mechanical systems. Proficient in advanced engineering software like Ansys, OpenFOAM, Matlab, SolidWorks, and programming frameworks such as TensorFlow, PyTorch, and Keras, Joel demonstrates versatile technical competence. His research areas encompass CFD modeling, predictive diagnostics, and intelligent control of mechatronic systems, contributing to sustainable mobility and cleaner energy solutions. A shortlisted nominee for the Best Researcher Award, he exemplifies academic excellence, innovation, and dedication to advancing knowledge at the interface of engineering and artificial intelligence, with aspirations to drive impactful contributions in energy optimization and sustainable technologies.

 Profile: ORCID

Featured Publications

Applied Sciences (2025)
Ebolembang, J. F., Nkol, F. P. N., Tabejieu, L. M. A., Nono, F. T., & Abbe, C. V. N. (2025). Prediction of combustion parameters and pollutant emissions of a dual-fuel engine based on recurrent neural networks. Applied Sciences, 15(18), 9868.

International Journal of Heat and Technology (2023)
Nkol, F. P. N., Ebolembang, J. F., Banta, N. J. I., Yotchou, G. V. T., Abbe, C. V. N., & Mouangue, R. M. (2023). Simulating the effect of methanol and spray tilt angle on pollutant emission of a diesel engine using different turbulence models. International Journal of Heat and Technology, 41(5), 105–1120.