IoT network attack detection using XAI and reliability analysis

Abstract

IoT has emerged as one of the most sophisticated techniques in recent years. But inadequate security controls are the most typical barrier to IoT expansion as the devices transmit a huge amount of data. Nowadays different machine learning and deep learning models are used to detect various IoT attacks. In our previous research, we applied Decision Tree, Random Forest, AdaBoost, XGBoost, ANN, and MLP to the IoT/IIoT dataset of TON IoT datasets to classify IoT network attacks. In binary classification, we got above 96% accuracy for all methods. In contrast, AdaBoost and ANN underperformed in multiclass classification. As accuracy improves, models get more complicated, and these models are frequently seen as black boxes that are difficult to interpret. Though these models give highly precise results, an explanation is required in order to comprehend and accept the models' decisions. Here comes XAI which emphasizes a variety of ways for breaking the black-box nature of Machine Learning and Deep Learning models as well as delivering human-level explanations. In this article, we have extended our work by analyzing different machine learning and deep learning methodologies using XAI to explain the categorization of IoT network attacks. LIME, SHAP, and ELI5 approaches have been used to interpret and explain which will increase transparency and reliability.