Intrusion detection in the internet of things using convolutional neural networks: an explainable AI approach

Abstract Intrusion Detection Systems (IDSs) with a Machine Learning (ML) technique have shown efficacy in securing Internet of Things (IoT) networks in recent years. As cyber threats continue to evolve, IDS have become increasingly reliant on advanced ML and deep learning (DL) techniques to improve detection accuracy. However, the growing complexity of these models often makes it challenging for security analysts to interpret the reasoning behind specific alerts. While extensive research has been conducted on IDS using ML and DL methods, the issue of interpretability remains largely unaddressed. One of the interpretable methods in machine learning is to use model-agnostic interpretation tools that can be applied to any supervised machine learning model. To address this issue, a new hybrid model composed of a lightweight one-dimensional convolutional Neural Network (1D-CNN) is proposed with the interpretation ability of the results in which, resource-constrained IoT devices can execute the proposed model. In the first phase, the SHapley Additive exPlanations (SHAP) technique is used for feature selection to detect the most important features. These features can be consider