EL-ID-BID: Ensemble Stacking-Based Intruder Detection in BoT-IoT Data

The Internet of Things continues to grow in size, connection, and applicability. Just like other new technologies, this ecosystem affects every area of our daily existence (Kafle et al. in IEEE Commun Mag 54:43–49, 2016). Despite the many advantages of the Internet of Things (IoT), the importance of securing its expanded attack surface has never been higher. There has been a recent increase in reports of botnet threats moving into the Internet of Things (IoT) environment. As a result, finding effective methods to secure IoT systems is a critical and challenging area of study. Potentially useful alternatives include methods based on machine learning, which can identify suspicious activities and even uncover network attacks. Simply relying on one machine learning strategy may lead to inaccuracies in data collection, processing, and representation if applied in practice. This research uses stacked ensemble learning to detect attacks better than conventional learning, which uses one algorithm for intruder detection (ID). To evaluate how well the stacked ensemble system performs in comparison to other common machine learning algorithms like Decision Tree (DT), random forest (RF), Naive Bayes (NB), and support vector machine (SVM), the BoT-IoT benchmark dataset has been used. Based on the findings of the experiments, stacked ensemble learning is the best method for classifying attacks currently available. Our experimental outcomes were assessed for validation data set, accuracy, precision, recall, and F1-score. Our results were competitive with best accuracy and ROC values when benchmarked against existing research.