AI Research Analysis
Securing Radiation Detection Systems with an Efficient TinyML-Based IDS for Edge Devices
This paper introduces a novel approach to enhance the cybersecurity of Radiation Detection Systems (RDSs) through an optimized TinyML-based Intrusion Detection System (IDS). By deploying advanced machine learning capabilities directly on resource-constrained edge devices, it significantly reduces computational demands and inference time, ensuring real-time threat detection with high accuracy.
Authored by: Einstein Rivas Pizarro, Wajiha Zaheer, Li Yang, Khalil El-Khatib, Glenn Harvel.
Executive Impact & Key Performance Gains
Leveraging TinyML techniques, this research delivers substantial improvements in operational efficiency and security posture for critical infrastructure.
0%
Inference Time Reduced
0%
Memory Footprint Reduced
0%
Detection Accuracy
Real-time
Edge Intelligence
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
This section explores how TinyML fundamentally changes the landscape of cybersecurity for critical infrastructure, particularly Radiation Detection Systems (RDS).
97.3%
Reduction in Model Memory Footprint (from 274.6 KB to 7.4 KB)
Enterprise Process Flow: TinyML IDS Development
1. Data Generation & Simulation
→
2. ML Model Selection
→
3. Feature Selection & Sampling
→
4. Pruning & Quantization
→
5. Edge Deployment & Validation
| Metric | Traditional XGBoost | TinyML XGBoost |
|---|---|---|
| Accuracy | 99.835% | 99.806% |
| F1 Score | 99.835% | 99.806% |
| Inference Time (sec) | 0.2684 | 0.0633 |
| Inference Time (ms/sample) | 8.8357 | 0.0064 |
| Memory Usage (KB) | 274.6221 | 7.4375 |
| Cell Execution Time (sec) | 0.2684 | 0.0633 |
Enhanced Security for Critical Infrastructure
The implementation of TinyML-based Intrusion Detection Systems directly on edge devices significantly bolsters the security posture of Radiation Detection Systems (RDSs) and other critical IoT infrastructure. By enabling real-time detection of cyber-attacks such as data injection, MITM, and DDoS without reliance on cloud processing, the solution minimizes latency and enhances system resilience. This approach ensures immediate response capabilities, protecting public health and safety by maintaining the integrity and reliability of radiation measurements in sensitive environments. The optimized XGBoost model achieves 99.8% detection accuracy with vastly reduced computational demands, making it ideal for resource-constrained settings.
Calculate Your Potential AI ROI
Estimate the economic and efficiency gains your enterprise could achieve by integrating our optimized AI solutions.
Estimated Annual Savings
$0
Annual Hours Reclaimed
0
Our AI Implementation Roadmap
A structured approach to integrating TinyML IDS for maximum impact and minimal disruption.
Phase 1: Data Synthesis & Preprocessing
Create a tailored RDS dataset by leveraging K-Means clustering, introducing correlated noise, and simulating diverse cyber-attacks for robust model training.
Phase 2: Baseline Model Evaluation & Selection
Evaluate traditional ML models (RF, XGBoost, LightGBM, CatBoost, LSTM) to identify the most effective performer for the specific RDS dataset, balancing accuracy with initial resource considerations.
Phase 3: TinyML Optimization Pipeline
Apply advanced TinyML techniques including feature selection, undersampling to address class imbalance, hyperparameter tuning with Optuna, pruning, and ONNX-based dynamic quantization to significantly reduce model complexity and computational footprint.
Phase 4: Edge Deployment & Validation
Deploy the optimized TinyML model on resource-constrained edge devices for real-time intrusion detection, rigorously evaluating its performance metrics (inference time, memory usage, accuracy) in practical scenarios.
Ready to Secure Your Critical Systems with AI?
Connect with our experts to design a tailored TinyML IDS solution for your organization.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat