Search Results (4)

In this paper, the authors analyze the applicability of artificial intelligence algorithms for classifying file encryption methods based on statistical features extracted from the binary content of files. The prepared datasets included both unencrypted files and files encrypted using selected cryptographic algorithms in Electronic Codebook (ECB) and Cipher Block Chaining (CBC) modes. These datasets were further diversified by varying the number of encryption keys and the sample sizes. Feature extraction focused solely on basic statistical parameters, excluding an analysis of file headers, keys, or internal structures. The study evaluated the performance of several models, including Random Forest, Bagging, Support Vector Machine, Naive Bayes, K-Nearest Neighbors, and AdaBoost. Among these, Random Forest and Bagging achieved the highest accuracy and demonstrated the most stable results. The classification performance was notably better in ECB mode, where no random initialization vector was used. In contrast, the increased randomness of data in CBC mode resulted in lower classification effectiveness, particularly as the number of encryption keys increased. This paper provides a comprehensive analysis of the classifiers’ performance across various encryption configurations and suggests potential directions for further experiments. Full article

This paper focuses on a block cipher adaptation of the Galois Extension Fields (GEF) combination technique for PRNGs and targets application in the Internet of Things (IoT) space, an area where the combination technique was concluded as a quality stream cipher. Electronic Codebook (ECB) and Cipher Feedback (CFB) variations of the cryptographic algorithm are discussed. Both modes offer computationally efficient, scalable cryptographic algorithms for use over a simple combination technique like XOR. The cryptographic algorithm relies on the use of quality PRNGs, but adds an additional layer of security while preserving maximal entropy and near-uniform distributions. The use of matrices with entries drawn from a Galois field extends this technique to block size chunks of plaintext, increasing diffusion, while only requiring linear operations that are quick to perform. The process of calculating the inverse differs only in using the modular inverse of the determinant, but this can be expedited by a look-up table. We validate this GEF block cipher with the NIST test suite. Additional statistical tests indicate the condensed plaintext results in a near-uniform distributed ciphertext across the entire field. The block cipher implemented on an MSP430 offers a faster, more power-efficient alternative to the Advanced Encryption Standard (AES) system. This cryptosystem is a secure, scalable option for IoT devices that must be mindful of time and power consumption. Full article

In this paper, we present the first optimized implementation of ARIA block cipher on low-end 8-bit Alf and Vegard’s RISC processor (AVR) microcontrollers. To achieve high-speed implementation, primitive operations, including rotation operation, a substitute layer, and a diffusion layer, are carefully optimized for the target low-end embedded processor. The proposed ARIA implementation supports the electronic codebook (ECB) and the counter (CTR) modes of operation. In particular, the CTR mode of operation is further optimized with the pre-computed table of two add-round-key, one substitute layer, and one diffusion layer operations. Finally, the proposed ARIA-CTR implementations on 8-bit AVR microcontrollers achieved 187.1, 216.8, and 246.6 clock cycles per byte for 128-bit, 192-bit, and 256-bit security levels, respectively. Compared with previous reference implementations, the execution timing is improved by 69.8%, 69.6%, and 69.5% for 128-bit, 192-bit, and 256-bit security levels, respectively. Full article

Because of the ubiquity of Internet of Things (IoT) devices, the power consumption and security of IoT systems have become very important issues. Advanced Encryption Standard (AES) is a block cipher algorithm is commonly used in IoT devices. In this paper, the power consumption and cryptographic calculation requirement for different payload lengths and AES encryption types are analyzed. These types include software-based AES-CB, hardware-based AES-ECB (Electronic Codebook Mode), and hardware-based AES-CCM (Counter with CBC-MAC Mode). The calculation requirement and power consumption for these AES encryption types are measured on the Texas Instruments LAUNCHXL-CC1310 platform. The experimental results show that the hardware-based AES performs better than the software-based AES in terms of power consumption and calculation cycle requirements. In addition, in terms of AES mode selection, the AES-CCM-MIC64 mode may be a better choice if the IoT device is considering security, encryption calculation requirement, and low power consumption at the same time. However, if the IoT device is pursuing lower power and the payload length is generally less than 16 bytes, then AES-ECB could be considered. Full article