A Hybrid SHAP-RNN Model for Predicting and Explaining DDoS Attacks on IoT Networks


Ahmad Mater Aljohani and Ibrahim Elgendi, University of Canberra, Australia


This study focuses on applying explainable artificial intelligence approaches to solve the challenge of attack detection in Internet of Things networks. The paper emphasizes how the proposed intrusion detection platform might enhance the model ability of explaining and comprehending attacks that might take place. The study suggests theoretical and practical advancements that directly affect the intrusion detection in Internet of Things networks. To comprehend how the system operates and how attacks happen, this paper describes the architecture and structure of the introduced explainable model. The targeted contributions focus on identifying possible attacks and interpreting taken decisions. The findings of testing on the UNSW-NB15 dataset show a better performance of RNN-SHAP model when compared to other algorithms (GRU, SVM, and RL), in terms of numerous metrics such as accuracy (98.98%), precision (100%), recall (93.44%), and F1-score (95.63%).


Attack detection, explainable artificial intelligence (XAI), deep learning (DL), Recurrent neural networks (RNN), SHapley Additive explanation (SHAP), Distributed Denial of Service (DDoS)

Full Text  Volume 14, Number 6