Abstract
The advancement of Internet of Things (IoT) technologies leads to a wide penetration and large-scale deployment of IoT systems across an entire city or even country. While IoT systems are capable of providing intelligent services, the large amount of data collected and processed in IoT systems also raises serious security concerns. Many research efforts have been devoted to design intelligent network intrusion detection system (NIDS) to prevent misuse of IoT data across smart applications. However, existing approaches may suffer from the issue of limited and imbalanced attack data when training the detection model, which make the system vulnerable especially for those unknown type attacks. In this study, a novel hierarchical adversarial attack (HAA) generation method is introduced to realize the level-aware black-box adversarial attack strategy, targeting the graph neural network (GNN)-based intrusion detection in IoT systems with a limited budget. By constructing a shadow GNN model, an intelligent mechanism based on a saliency map technique is designed to generate adversarial examples by effectively identifying and modifying the critical feature elements with minimal perturbations. A hierarchical node selection algorithm based on random walk with restart (RWR) is developed to select a set of more vulnerable nodes with high attack priority, considering their structural features, and overall loss changes within the targeted IoT network. The proposed HAA generation method is evaluated using the open-source data set UNSW-SOSR2019 with three baseline methods. Comparison results demonstrate its ability in degrading the classification precision by more than 30% in the two state-of-the-art GNN models, GCN and JK-Net, respectively, for NIDS in IoT environments.
Highlights
THE proliferation of Internet of Things (IoT) technologies and systems are growing at an unprecedented rate
Advanced Internet of Things (IoT) networks and systems are growing at an unforeseen rate, reaching every corner of our cities and countries, to collect useful data and to offer intelligent services
Existing Network Intrusion Detection System (NIDS) approaches all suffer from the fact that there is only a limited amount of very imbalanced training data, which leads to the vulnerability against unknown types of malicious attack
Summary
THE proliferation of Internet of Things (IoT) technologies and systems are growing at an unprecedented rate. The collected IoT data contain sensitive information and require more attention on privacy protection and reliable data security issues To deal with such increasing privacy and security concerns, modern IoT or distributed systems need to be able to detect and prevent network intrusions in a more intelligent way. As a typical type of neural network in deep learning models, Graph Neural Network (GNN) has demonstrated its promising performance in dealing with graph or network data [12] It still suffers when facing limited or imbalanced training data, and can be vulnerable to adversarial attacks. I) An integrated framework for the level-aware black-box adversarial attack strategy is designed and constructed to compromise the GNN-based NIDS in typical IoT environments with limited budget.
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