Edge Implicit Weighting with graph transformers for robust intrusion detection in Internet of Things network

Abstract

In recent years, the Internet of Things devices have progressively deployed in various applications including smart cities, intelligent transportation, healthcare, and agriculture. However, this widespread adaptation of the Internet of Things networks has been vulnerable to several attacks. Lack of security protocols, unauthorized access, and improper device updates lead the Internet of Things environment to several attacks, which impact network security and confidentiality of users. This paper develops an innovative approach that integrates Edge Implicit Weighting and Aggregated Graph Transformer architecture for accurate and timely intrusion detection. The proposed technique aggregates information from both one-hop and two-hop neighbors to derive immediate and extended relational context thereby improving the detection of complex attacks. This approach designs an Edge Implicit Weighting mechanism that allows the model to prioritize structurally significant relationships and enhance the accuracy of attack detection. The multi-head attention mechanism is introduced to enhance the detection of relevant patterns even in highly variable traffic scenarios. Further, the proposed framework incorporates the Synthetic Minority Over-sampling Technique to generate synthetic samples of minority classes to reduce class imbalance problems and attain balanced detection performance across all classes. The performance of the proposed detection technique is analyzed using multiple datasets with standard evaluation parameters. The proposed technique achieves outstanding performance results including an accuracy of 98.87% and a recall of 98.36%. From this experimental validation, it's clear that the proposed framework provides robust performance under diverse network conditions and handles imbalanced data effectively.