Intrusion Detection System in Explainable Artificial Intelligence by Using Different Algorithms

The Internet of Things (IoT) is a rapidly established technology that combines various domains and such technology permits devices to process, transfer, and receive information without the involvement of humans. However, privacy and security problems remain a major difficulty in the IoT. An Intrusion Detection System (IDS) is needed for securing attacks on this platform. Recently, various researchers identified significant ways for ID by employing Explainable Artificial Intelligence (XAI) approaches to Machine Learning (ML) and Deep Learning (DL) techniques. This research indicates various methodologies like Balanced and Stacked Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), Extreme Gradient Boosting (XGB), Naïve Bayes (NB), Ada-Boost (AB), Cat-Boost (CB), Long Short-Term Memory (LSTM), Deep Neural Network (DNN), and Bidirectional-LSTM (Bi-LSTM) that are utilized for IDS. Accuracy, f1-score, recall, Area Under Curve (AUC), precision, score time, and False Alarm Rate (FAR) are employed as the performance metrics for this study.