Mathematical Modeling of Quantum-Secured Blockchain IDS for IoT Infrastructure Using Transformers

Abstract

The rapid growth of the Internet of Things (IoT) has increased the risk of cyberattacks, creating a need for intelligent and secure intrusion detection mechanisms. Traditional Intrusion Detection Systems (IDS) face challenges in identifying complex attack patterns and ensuring the authenticity and integrity of security alerts. This paper proposes a mathematical modelling framework for a quantum-secured blockchain-based IDS for IoT infrastructure using Transformer architecture. The proposed framework employs a Transformer-based model to analyse network traffic features and classify normal and malicious activities. Benchmark datasets, including CICIoT2023, TON_IoT, and Edge-IIoTset, can be used to evaluate the detection performance of the model. Post-quantum digital signatures based on the Module-Lattice-Based Digital Signature Algorithm (ML-DSA) are incorporated to authenticate security alerts and protect their integrity against potential quantum-computing threats. Furthermore, a permissioned blockchain maintains tamper-evident records of verified alerts, enabling traceability and secure auditing. The framework can be evaluated using accuracy, precision, recall, F1-score, false-positive rate, authentication overhead, transaction latency, and throughput. By integrating Transformer-based detection, post-quantum authentication, and blockchain technology, the proposed approach aims to provide a reliable and secure intrusion detection framework for IoT environments. Experimental validation is required to quantify its effectiveness and computational efficiency.