A block cipher algorithm identification scheme based on hybrid k-nearest neighbor and random forest algorithm

Abstract

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.

Post-quantum cryptographic algorithm identification using machine learning

“…In this research, the cryptograms of the AES and Blowfish algorithms in ECB encryption mode were submitted to the fifteen component tests of the NIST SP 800-22rev1a suite. The results obtained here are significantly superior to those obtained by YUAN, Ke et al in [34] and [35], where ciphertext files of 1, 8, 64, 256 and 512 KB were submitted to ten tests contained in this test battery. Special attention must be paid to the KNN classifier, which showed total accuracy in samples of 60KB.…”

“…In [33], Fan & Zhao employed three classifiers -RF, LR and SVM -to perform a distinction attack on DES, 3DES, AES-128, AES-256, IDEA, SMS4, Blowfish and Camellia-128 block ciphers. However, single-layer classifiers may present low accuracies, overfitting and difficulties to find adequate parameters according to [34].…”

Establishing a Prediction Model for Tea Leaf Moisture Content Using the Free-Space Method’s Measured Scattering Coefficient

“…RF is an ensemble learning algorithm based on DT [27] and can be roughly divided into a classification algorithm and a regression algorithm; this paper only uses the latter, the principle of which is based on a combination model composed of a set of regression decision subtrees.…”

Pharmaceutical distribution path optimization based on K-nearest neighbor algorithm

“…where equation (1) indicates that the sum of all demands of all customer nodes served by one vehicle should not be greater than the total carrying capacity of the vehicle; equation (2) indicates the calculated relationship between the moment when the vehicle arrives at the customer point and the moment when it leaves the customer point; equation (3) indicates the time from point i to point j; equation (4) indicates to be earlier than the latest delivery time; equation (5) indicates the elimination of subloops; and equation (6) indicates that each customer point is served and is served only once. The moment when the last customer is delivered in the line…”

“…liao et al [3] proposed a new method called KCNN to improve the performance of kNN performance by using prototype reduction to learn a reduced prototype set that can represent the original prototype set and verified that the proposed method has better robustness and convergence than CNN. wang et al [4] proposed a new local adaptive neighbor classification based on average distance in order to address the performance of KNN and finally experimentally verified the classification performance of the new method using 24 real datasets. Yuan et al [5] proposed a new ensemble learning based model named hybrid k-nearest neighbor and random forest, and a block cipher algorithm identification scheme was constructed, and the proposed scheme was used to perform binary and quintuple classification experiments on the block cipher algorithm, and finally its accuracy was verified experimentally.…”