用轻量级机器学习保护辐射检测系统免受网络攻击
Securing Radiation Detection Systems with an Efficient TinyML-Based IDS for Edge Devices
- 基于剪枝、量化等技术优化XGBoost模型，适配边缘设备
- 在资源受限设备上实现实时入侵检测，准确率达98.7%
- 专为辐射监测场景设计，适合工业与医疗安全领域
辐射检测系统（RDS）在核设施和医疗环境等关键场景中对公共安全至关重要。然而，这些系统正面临数据注入、中间人攻击、ICMP洪水、僵尸网络、权限提升及分布式拒绝服务（DDoS）等网络威胁，可能破坏辐射测量的完整性与可靠性，带来重大健康与安全风险。本文提出一种新的合成辐射数据集，并设计了一种面向资源受限环境的入侵检测系统（IDS）。该系统采用经过剪枝、量化、特征选择和采样优化的XGBoost模型，结合轻量级机器学习（TinyML）技术，显著降低模型体积与计算开销，在低功耗设备上实现近实时入侵检测，同时保持效率与精度之间的合理平衡。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。