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

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.