Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.01592 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915474431803392 |
|---|---|
| author | Pizarro, Einstein Rivas Zaheer, Wajiha Yang, Li El-Khatib, Khalil Harvel, Glenn |
| author_facet | Pizarro, Einstein Rivas Zaheer, Wajiha Yang, Li El-Khatib, Khalil Harvel, Glenn |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_01592 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Securing Radiation Detection Systems with an Efficient TinyML-Based IDS for Edge Devices Pizarro, Einstein Rivas Zaheer, Wajiha Yang, Li El-Khatib, Khalil Harvel, Glenn Cryptography and Security Artificial Intelligence Machine Learning Systems and Control 68T05, 93C65, 90C35 K.6.5; C.2.3; I.2.6 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. |
| title | Securing Radiation Detection Systems with an Efficient TinyML-Based IDS for Edge Devices |
| topic | Cryptography and Security Artificial Intelligence Machine Learning Systems and Control 68T05, 93C65, 90C35 K.6.5; C.2.3; I.2.6 |
| url | https://arxiv.org/abs/2509.01592 |