Deep PUF: A Highly Reliable DRAM PUF-Based Authentication for IoT Networks Using Deep Convolutional Neural Networks
- We propose deep PUF as a two-stage mechanism, including multi-label classification and challenge verification, to provide a robust and lightweight device authentication without error-correcting codes and other pre-filtering methods.
- We implement two types of latency-based proposals (tRCD and tRP PUFs) as the fast runtime accessible DRAM PUFs and analyze their characteristics to train the CNN.
- Finally, we develop a CNN model using experimental data and analyze the robustness and security of the proposed deep PUF.
2. Background and Motivation
2.1. DRAM Operation and Timing Parameters
2.2. DRAM PUF Technologies
Latency-Based DRAM PUFs
2.3. Post-Processing and Pre-Selection Algorithms
3. Proposed Deep PUF
- Robustness: determines the effects of different operating conditions on output responses. This property affects the similarity of samples in a single class and accuracy of classification results. Robustness of DRAM PUF can be calculated using intra-Hamming distance (HD) or intra-Jaccard index values.
- Uniqueness: enough difference between two responses using two distinct DRAM blocks results in uniqueness. This factor shows the difference of samples belonging to separate classes and can be determined by computing inter-class HD. Figure 3c depicts the developed deep CNN which is trained on the generated dataset and learns the failures behavior under various measurements.
- Stability of operating conditions: locating the PUF device in a stable ambiance in which the variety of conditions (e.g., temperature, voltage) is not appreciable, causes more consistency inside each class and results in better accuracy. Due to the PUF sensitivity to environmental conditions, in an environment with varying temperatures, the number of bit failures in each measurement and the way the failures are distributed may cause samples to be far different than usual. In this case, deep PUF requires involving the responses of all possible temperatures to extract entire failure features, thereby leading an accurate classification.
- Variety of blocks and input patterns: one scenario is to organize the classes using only a single memory block and writing different patterns into it as the challenges, and the other one is exploiting various blocks. If only one memory block is utilized to perform the PUF, it is necessary to provide the challenges based on different input data patterns. However, in the case of using multiple blocks, the challenges can be configured by the same data for all blocks.
3.2. Authentication Phase
- The received raw bits are classified using CNN, structured during the enrollment phase.
- The detected label is compared with the original challenge.
4. DRAM Experiments and Observations
5. Development of CNN Model
5.1. Dataset Creation
- The same input pattern (all “1”s) is used to characterize all blocks and the operating conditions are stable (room temperature and nominal voltage).
- Different input patterns (0x00, 0x01… 0xFF) are used for different blocks and the conditions are stable.
- The same input pattern is used to characterize all blocks and the operating conditions are unstable.
- Different input patterns are used for different blocks and the conditions are unstable.
5.2. Training the Classifier
|Algorithm 1 Convolutional neural network (CNN)-based classification|
|Input: a set of challenges, including the address of PUF segment and input pattern: ()|
|Output: collections of images corresponding to different challenges: ()|
|Process: // build N folders: folder_1, folder_2,…, folder_N|
|//N: number of classes|
|// M: number of measurements|
|//Convert the to integer values and a gray-scale image|
|Input: a collection of labeled images (responses):|
|Output: a collection of features assigned to different labels|
|// e: number of epochs|
|// N: number of classes, M: number of samples for each class|
|// after applying the defined layers|
|Input: // testing sample|
|// p: the probability vector calculated by Softmax|
5.3. Performance Metrics
6. Security Analysis and Discussion
6.1. Security and Robustness
6.2. Performance Comparisons
6.3. Security Discussion and Countermeasures against Possible Attacks
7. Conclusion and Future Work
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|Reduced time||5 ns|
|Block size||200 Kb|
|Input pattern||All “1” s|
|Number of tested blocks||200|
|Mechanism||Average Inter-HD||Average Probability of Failure|
(for 200 Blocks)
|Mechanism||Stable Environmental Conditions||Unstable Environmental Conditions|
|Intra-Class HD||Inter-Class HD||Intra-Class HD||Inter-Class HD|
|Total number of images in each dataset||9000|
|Number of classes (challenges)||100|
|Number of samples (responses)||90|
|Tested temperatures||25–55 °C|
|Percentage of training data||80|
|Percentage of test data||20|
|The resolution of images (pixels)||222 × 222|
|Convolution 2D||(222, 222, 128)|
|Convolution 2D||(220, 220, 32)|
|Max pooling||(109, 109, 32)|
|Convolution 2D||(107, 107, 16)|
|Convolution 2D||(105, 105, 32)|
|Max pooling||(52, 52, 32)|
|Convolution 2D||(50, 50, 16)|
|Max pooling||(24, 24, 16)|
|Dense||Number of classes|
|Accuracy of Classification (%)|
|Same Input Pattern for All Blocks||Different Input Patterns|
|Augmented Data||Original Data||Augmented Data||Original Data|
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Najafi, F.; Kaveh, M.; Martín, D.; Reza Mosavi, M. Deep PUF: A Highly Reliable DRAM PUF-Based Authentication for IoT Networks Using Deep Convolutional Neural Networks. Sensors 2021, 21, 2009. https://doi.org/10.3390/s21062009
Najafi F, Kaveh M, Martín D, Reza Mosavi M. Deep PUF: A Highly Reliable DRAM PUF-Based Authentication for IoT Networks Using Deep Convolutional Neural Networks. Sensors. 2021; 21(6):2009. https://doi.org/10.3390/s21062009Chicago/Turabian Style
Najafi, Fatemeh, Masoud Kaveh, Diego Martín, and Mohammad Reza Mosavi. 2021. "Deep PUF: A Highly Reliable DRAM PUF-Based Authentication for IoT Networks Using Deep Convolutional Neural Networks" Sensors 21, no. 6: 2009. https://doi.org/10.3390/s21062009