Advanced intrusion detection in internet of things using graph attention networks

Abstract

Abstract Internet of Things (IoT) denotes a system of interconnected devices equipped with processors, sensors, and actuators that capture and exchange meaningful data with other smart systems. IoT technology has been successfully applied across various sectors, including agriculture, supply chain management, education, healthcare, traffic control, and utility services. However, the diverse range of IoT nodes introduces significant security challenges. Common Internet of Things safety features like encryption, authentication, and access control frequently fall short of meeting their desired functions. In this paper, we present a novel perspective to IoT security by using a Graph-based (GB) algorithm to construct a graph that is evaluated with a graph-based learning Intrusion Detection System (IDS) incorporating a Graph Attention Network (GAT). In addition, we leveraged a small benchmark NSL-KDD dataset to conduct detailed performance evaluation of the GNN model by focusing on essential key metrics such as F1-score, recall, accuracy, and precision to guarantee comprehensive analysis. Our findings validate the effectiveness of the GNN-based IDS in detecting intrusions, which highlights its robustness and scalability in mitigating the evolving security challenges within IoT systems.

Keywords

• IoT
• IDS
• Machine learning
• NSLKDD dataset
• GNN