An Efficient Intrusion Detection System for Safeguarding Radiation Detection Systems

• URL: http://arxiv.org/abs/2509.01599v1
• Date: Mon, 01 Sep 2025 16:31:46 GMT
• Title: An Efficient Intrusion Detection System for Safeguarding Radiation Detection Systems
• Authors: Nathanael Coolidge, Jaime González Sanz, Li Yang, Khalil El Khatib, Glenn Harvel, Nelson Agbemava, I Putu Susila, Mehmet Yavuz Yagci,
• Abstract summary: Radiation Detection Systems (RDSs) are used to measure and detect abnormal levels of radioactive material in the environment.<n>These systems lack protection against malicious external attacks to modify the data.<n>A common attack on RDSs is Denial of Service (DoS), where the attacker aims to overwhelm the system, causing malfunctioning RDSs.<n>This paper proposes an efficient Machine Learning (ML)-based IDS to detect anomalies in radiation data, focusing on DoS attacks.
• Score: 3.0341074926328044
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Online Reliable Anomaly Detection via Neuromorphic Sensing and Communications [58.796149594878585]
  This paper proposes a low-power online anomaly detection framework based on neuromorphic wireless sensor networks.<n>In the considered system, a central reader node actively queries a subset of neuromorphic sensor nodes (neuro-SNs) at each time frame.<n>The neuromorphic sensors are event-driven, producing spikes in correspondence to relevant changes in the monitored system.
  arXiv  Detail & Related papers  (2025-10-16T13:56:54Z)
• LIGHT-HIDS: A Lightweight and Effective Machine Learning-Based Framework for Robust Host Intrusion Detection [10.78145758065258]
  The expansion of edge computing has increased the attack surface, creating an urgent need for robust, real-time machine learning (ML)-based host intrusion detection systems (HIDS)<n>This paper proposes LIGHT-HIDS, a lightweight machine learning framework that combines a compressed neural network feature extractor trained via Deep Support Vector Data Description (DeepSVDD) with an efficient novelty detection model.<n> Experimental results on multiple datasets demonstrate that LIGHT-HIDS consistently enhances detection accuracy while reducing inference time by up to 75x compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-09-16T19:02:23Z)
• Securing Radiation Detection Systems with an Efficient TinyML-Based IDS for Edge Devices [3.5216201054915692]
  Radiation Detection Systems (RDSs) play a vital role in ensuring public safety across various settings.<n>These systems are increasingly vulnerable to cyber-attacks.<n>This paper presents a new synthetic radiation dataset and an Intrusion Detection System (IDS) tailored for resource-constrained environments.
  arXiv  Detail & Related papers  (2025-09-01T16:26:37Z)
• DataSentinel: A Game-Theoretic Detection of Prompt Injection Attacks [101.52204404377039]
  LLM-integrated applications and agents are vulnerable to prompt injection attacks.<n>A detection method aims to determine whether a given input is contaminated by an injected prompt.<n>We propose DataSentinel, a game-theoretic method to detect prompt injection attacks.
  arXiv  Detail & Related papers  (2025-04-15T16:26:21Z)
• Learning in Multiple Spaces: Few-Shot Network Attack Detection with Metric-Fused Prototypical Networks [47.18575262588692]
  We propose a novel Multi-Space Prototypical Learning framework tailored for few-shot attack detection.<n>By leveraging Polyak-averaged prototype generation, the framework stabilizes the learning process and effectively adapts to rare and zero-day attacks.<n> Experimental results on benchmark datasets demonstrate that MSPL outperforms traditional approaches in detecting low-profile and novel attack types.
  arXiv  Detail & Related papers  (2024-12-28T00:09:46Z)
• Optimized IoT Intrusion Detection using Machine Learning Technique [0.0]
  Intrusion detection systems (IDSs) are essential for defending against a variety of attacks.<n>The functional and physical diversity of IoT IDS systems causes significant issues.<n>For peculiarity-based IDS, this study proposes and implements a novel component selection and extraction strategy.
  arXiv  Detail & Related papers  (2024-12-03T21:23:54Z)
• Convolutional Neural Network Design and Evaluation for Real-Time Multivariate Time Series Fault Detection in Spacecraft Attitude Sensors [41.94295877935867]
  This paper presents a novel approach to detecting stuck values within the Accelerometer and Inertial Measurement Unit of a drone-like spacecraft. A multi-channel Convolutional Neural Network (CNN) is used to perform multi-target classification and independently detect faults in the sensors. An integration methodology is proposed to enable the network to effectively detect anomalies and trigger recovery actions at the system level.
  arXiv  Detail & Related papers  (2024-10-11T09:36:38Z)
• Towards a Privacy-preserving Deep Learning-based Network Intrusion Detection in Data Distribution Services [0.0]
  Data Distribution Service (DDS) is an innovative approach towards communication in ICS/IoT infrastructure and robotics. Traditional intrusion detection systems (IDS) do not detect any anomalies in the publish/subscribe method. This report presents an experimental work on simulation and application of Deep Learning for their detection.
  arXiv  Detail & Related papers  (2021-06-12T12:53:38Z)
• Time-Based CAN Intrusion Detection Benchmark [0.0]
  Vehicle control systems are vulnerable to message injection attacks. Time-based intrusion detection systems (IDSs) have been proposed to detect these messages. We benchmark four time-based IDSs against the newly published ROAD dataset. We also develop an after-market plug-in detector using lightweight hardware.
  arXiv  Detail & Related papers  (2021-01-14T18:33:19Z)
• Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection [66.05992706105224]
  In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.
  arXiv  Detail & Related papers  (2020-08-05T19:29:35Z)
• Data Mining with Big Data in Intrusion Detection Systems: A Systematic Literature Review [68.15472610671748]
  Cloud computing has become a powerful and indispensable technology for complex, high performance and scalable computation. The rapid rate and volume of data creation has begun to pose significant challenges for data management and security. The design and deployment of intrusion detection systems (IDS) in the big data setting has, therefore, become a topic of importance.
  arXiv  Detail & Related papers  (2020-05-23T20:57:12Z)
• Multi-stage Jamming Attacks Detection using Deep Learning Combined with Kernelized Support Vector Machine in 5G Cloud Radio Access Networks [17.2528983535773]
  This research focuses on deploying a multi-stage machine learning-based intrusion detection (ML-IDS) in 5G C-RAN. It can detect and classify four types of jamming attacks: constant jamming, random jamming, jamming, and reactive jamming. The final classification accuracy of attacks is 94.51% with a 7.84% false negative rate.
  arXiv  Detail & Related papers  (2020-04-13T17:21:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.