Cryptographic algorithm identification, which refers to analyzing and identifying the encryption algorithm used in cryptographic system, is of great significance to cryptanalysis. In order to improve the accuracy of identification work, this article proposes a new ensemble learning-based model named hybrid k-nearest neighbor and random forest (HKNNRF), and constructs a block cipher algorithm identification scheme. In the ciphertext-only scenario, we use NIST randomness test methods to extract ciphertext features, and carry out binary-classification and five-classification experiments on the block cipher algorithms using proposed scheme. Experiments show that when the ciphertext size and other experimental conditions are the same, compared with the baselines, the HKNNRF model has higher classification accuracy. Specifically, the average binary-classification identification accuracy of HKNNRF is 69.5%, which is 13%, 12.5%, and 10% higher than the single-layer support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF) respectively. The five-classification identification accuracy can reach 34%, which is higher than the 21% accuracy of KNN, the 22% accuracy of RF and the 23% accuracy of SVM respectively under the same experimental conditions.

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Optical physical unclonable functions (PUFs) have great potentials in the security identification of Internet of Things. In this work, electrospun nanofibers are proposed as a candidate for a nanoscale, robust, stable and scalable PUF. The dark-field reflectance images of the polymer fibers are quantitatively analyzed by Hough transform. We find that the fiber length and orientation distribution reach an optimal point as the fiber density grows up over 850 in 400 x 400 pixels for a polyvinylpyrrolidone nanofiber based PUF device. Subsequently, we test the robustness and randomness of the PUF pattern by using the fiber amount as an encoding feature, generating a reconstruction success rate over 80% and simultaneously an entropy of 260 bits within a mean size of 4 cm2. A scale-invariant algorithm is adopted to identify the uniqueness of each pattern on a 256-sensor device. Furthermore, thermo-, moisture as well as photostability of the authentication process are systematically investigated by comparing polyacrylonitrile to polyvinylpyrrolidone system.