Securing Radiation Detection Systems with an Efficient TinyML-Based IDS for Edge Devices
This work addresses security risks in critical infrastructure like nuclear and medical settings, but it is incremental as it applies existing TinyML techniques to a new domain.
The paper tackles the vulnerability of Radiation Detection Systems to cyber-attacks by developing a TinyML-based Intrusion Detection System for edge devices, achieving real-time detection with reduced model size and computational demands while maintaining efficiency and accuracy.
Radiation Detection Systems (RDSs) play a vital role in ensuring public safety across various settings, from nuclear facilities to medical environments. However, these systems are increasingly vulnerable to cyber-attacks such as data injection, man-in-the-middle (MITM) attacks, ICMP floods, botnet attacks, privilege escalation, and distributed denial-of-service (DDoS) attacks. Such threats could compromise the integrity and reliability of radiation measurements, posing significant public health and safety risks. This paper presents a new synthetic radiation dataset and an Intrusion Detection System (IDS) tailored for resource-constrained environments, bringing Machine Learning (ML) predictive capabilities closer to the sensing edge layer of critical infrastructure. Leveraging TinyML techniques, the proposed IDS employs an optimized XGBoost model enhanced with pruning, quantization, feature selection, and sampling. These TinyML techniques significantly reduce the size of the model and computational demands, enabling real-time intrusion detection on low-resource devices while maintaining a reasonable balance between efficiency and accuracy.