用轻量机器学习检测辐射系统的拒绝服务攻击，实时高效。

Radiation Detection Systems (RDSs) are used to measure and detect abnormal levels of radioactive material in the environment. These systems are used in many applications to mitigate threats posed by high levels of radioactive material. However, these systems lack protection against malicious external attacks to modify the data. The novelty of applying Intrusion Detection Systems (IDS) in RDSs is a crucial element in safeguarding these critical infrastructures. While IDSs are widely used in networking environments to safeguard against various attacks, their application in RDSs is novel. A common attack on RDSs is Denial of Service (DoS), where the attacker aims to overwhelm the system, causing malfunctioning RDSs. This paper proposes an efficient Machine Learning (ML)-based IDS to detect anomalies in radiation data, focusing on DoS attacks. This work explores the use of sampling methods to create a simulated DoS attack based on a real radiation dataset, followed by an evaluation of various ML algorithms, including Random Forest, Support Vector Machine (SVM), logistic regression, and Light Gradient-Boosting Machine (LightGBM), to detect DoS attacks on RDSs. LightGBM is emphasized for its superior accuracy and low computational resource consumption, making it particularly suitable for real-time intrusion detection. Additionally, model optimization and TinyML techniques, including feature selection, parallel execution, and random search methods, are used to improve the efficiency of the proposed IDS. Finally, an optimized and efficient LightGBM-based IDS is developed to achieve accurate intrusion detection for RDSs.