ENTERPRISE AI ANALYSIS
A novel deep learning-based statistical randomness evaluation test methodology for cryptographic applications
The security of cryptographic systems is directly linked to the statistical randomness properties of the random numbers used. Traditional statistical randomness tests can be limited in evaluating the properties of these numbers and can require long processing times. While widely used test suites such as the National Institute of Standards and Technology (NIST) Special Publication (SP) 800–22 play a crucial role in assessing data randomness, they are slow on large data sets, can only evaluate certain statistical properties, and fail to detect complex data patterns and dependency structures. In this paper, we propose a new deep learning (DL) based method to overcome these limitations. In the study, bit sequences produced by an FPGA-based true random number generator (TRNG) and different pseudo-random number generators (PRNGs) were converted into image format, and classification experiments were carried out on the AlexNet, ResNet50, and EfficientNetB0 architectures. The results showed that AlexNet, with 87% accuracy and 99% recall, was by far the most successful method compared to the other models. Furthermore, the ablation analysis revealed that the components of data augmentation, early stopping, and cross-validation play a critical role in the model's stability and generalizability. Providing a reliable randomness evaluation with high accuracy, this new method, as an alternative to traditional statistical test suites, demonstrates that AI-based solutions can enhance the effectiveness and accuracy of randomness assessment in cryptographic applications. The success of DL methods in complex data structures offers significant potential for improving the security of modern cryptographic systems.
Authors: Seyfullah Kaner, Ali Murat Garipcan, Ebubekir Erdem
Publication Date: 17 October 2025
EXECUTIVE IMPACT
Leveraging deep learning for randomness testing provides critical enhancements for cryptographic security, ensuring robust and reliable systems.
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Accuracy with AlexNet
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Recall with AlexNet
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Processing Speed Improvement
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Enhanced Security Detection
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 details the innovative deep learning-based methodology, specifically the use of CNN architectures like AlexNet, ResNet50, and EfficientNetB0, for evaluating the statistical randomness of bit sequences. It covers data acquisition from FPGA-based TRNGs and PRNGs, conversion to image format, and the training and validation processes.
Deep Learning Model Randomness Test Flow
Obtaining data as .mif file via Block RAM
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Converting files to .txt files in accordance with NIST testing
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Converting bit sequences into images
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Training the AlexNet deep learning model
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Saving model weights as hdf5 file
87%
AlexNet achieved the highest performance.
| Model | Accuracy | Precision | Recall | F1 Score |
|---|---|---|---|---|
| AlexNet | 0.87 | 0.79 | 0.99 | 0.88 |
| ResNet50 | 0.50 | 0.50 | 1.00 | 0.67 |
| EfficientNetB0 | 0.56 | 0.57 | 0.89 | 0.69 |
This section delves into the statistical reliability analysis using bootstrap methods, providing a more comprehensive understanding of model sensitivity to sampling variability beyond single accuracy or F1 scores. It highlights AlexNet's stable performance and the variable performance of ResNet50 and EfficientNetB0.
0.999
AlexNet Mean F1-score (Bootstrap)
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EfficientNetB0 Mean F1-score (Bootstrap)
| Component Removed | Validation Accuracy (%) |
|---|---|
| Original (Full Model) | 87 |
| No Data Augmentation | 94.72 |
| No Early Stopping | 88.16 |
| No Cross-Validation | 100 |
This part discusses the broader implications of the deep learning approach for cryptographic security, emphasizing its ability to detect complex dependency structures and long-term patterns that traditional statistical tests may overlook. It highlights the potential for AI-assisted methods to contribute to new security standards.
Enhanced Cryptographic Security
The proposed deep learning approach significantly enhances cryptographic security by enabling the detection of intricate patterns and dependencies in random number sequences that traditional statistical tests often miss. This capability is crucial for identifying vulnerabilities against sophisticated cryptographic attacks. The AlexNet model's superior performance in distinguishing between random and non-random sequences offers a robust method for validating the security of TRNGs and PRNGs. This advancement paves the way for more resilient key generation and secure session management in modern cryptographic systems.
87%
Overall AlexNet Accuracy for Crypto Security
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Implementation Roadmap
A structured approach to integrating AI-based randomness testing into your enterprise workflows.
Phase 1: Data Acquisition & Preprocessing
Collect and transform bit sequences from TRNGs and PRNGs into image format, applying NIST SP 800-22 labeling. Establish balanced datasets for unbiased training.
Phase 2: Model Training & Optimization
Train selected deep learning architectures (e.g., AlexNet) using the prepared datasets. Optimize hyperparameters, employ data augmentation, early stopping, and cross-validation for robustness.
Phase 3: Validation & Performance Evaluation
Rigorously validate the trained models against unseen data, assessing accuracy, precision, recall, and F1-score. Conduct statistical reliability analysis using bootstrap methods.
Phase 4: Integration & Deployment
Integrate the validated DL model into existing cryptographic testing workflows. Deploy the solution to enhance real-time randomness assessment and security protocols.
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