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Sourav Kumar Bhoi

Work place: Department of Computer Science and Engineering, Parala Maharaja Engineering College (Govt.), Berhampur, 761003, India

E-mail: sourav.cse@pmec.ac.in

Website:

Research Interests: Machine Learning

Biography

Sourav Kumar Bhoi (Senior Member, IEEE and Member, ACM) Sourav Kumar Bhoi received the Ph.D. degree and M.Tech degree from the Department of Computer Science and Engineering, National Institute of Technology (NIT), Rourkela, India, in 2017 and 2013, respectively. He is currently an Assistant Professor with the Department of Computer Science and Engineering, Parala Maharaja Engineering College (a Government Engineering College), Berhampur, India. He has nearly ten years of teaching and research experience. He also completed his Post Doc in CSIS programme during 2022-23 from SU, MAN. He also completed a three-month CSIR Summer Research Training Program in online mode from NEIST, Jorhat, Assam (Government of India), in August 2020. He acted as Project investigators for research projects sponsored by MHRD and AICTE. He is an India Book of Record Holder for his research work, in 2022. He has more than 140 research publications in reputed international journals, conferences, books, book chapters, technical articles, patents, and theses. His research interests include machine learning, the Internet of Things, edge and fog computing, ad hoc and sensor networks, and information security. He is a member of many professional bodies, such as a member of IAENG, a Life Member of CSI, an Associate Member of IEI, and a fellow of SIESRP. He acted as a member of TPC and the session chair of many international conferences. He received the prestigious IET Premium Award from IET Networks journal, in 2016. He also received many other awards and honors, such as the University Foundation Day Faculty Research Award in CSE and the Sadananda Memorial Award from the Institution of Engineers (India), in 2021 and 2020, respectively. He was a reviewer for many international journals and conferences. He delivers several invited talks in reputed organizations. 

Author Articles
Securing Fog-assisted IoT: An Adaptable and Efficient Threat Identification Approach

By Surya Pavan Kumar Gudla Sourav Kumar Bhoi Kshira Sagar Sahoo GNV Rajareddy

DOI: https://doi.org/10.5815/ijcnis.2026.02.05, Pub. Date: 8 Apr. 2026

The increase in cyber attacks leads to significant challenges to the security in Fog based IoT environments. Existing studies have been implementing machine learning (ML), ensemble learning (EL) and deep learning (DL) for security, in this study we opted deep ensemble learning (DEL) for detection of threats in fog based IoT environments. The proposed DEL model is build using Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Gated Re-current Units (GRU) as base models, and it is augmented using a metalearner such as Logistic Regression (LR), Random Forest (RF), AdaBoost, XGBoost, CatBoost, and LightGBM and also with a Voting Classifier (VC) for f inding the best model. In our experimentation, the evaluation is performed with different datasets such as DDoS SDN, NSL-KDD, UNSW-NB-15, and IoTID20. In this work, DEL with RF achieved better performance than other models when performance metrics such as accuracy (Acy), precision (Prn), recall (Rcl), F1-Score (F1-S) and AUC-score are considered. For instance, DEL with RF achieved an Acy of 99.99%, Prn of 100%, Rcl of 99.96%, F1-S of 99.98% and AUC-score of 1.00 on IoTID20 dataset. Afterward, to analyze the network performance of the DEL models at fog, we have considered the metrics such as cost, energy, resource utilization, latency and service time. This work shows that DELmodels can improve the security of fog assisted IoT systems.

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