A survey on the cryptanalysis of the advanced encryption standard

The Advanced Encryption Standard (AES) is a cipher adopted by the National Institute of Standards and Technology (NIST) to secure classified United States (US) digital government documents. A cipher is an algorithm that converts information (plaintext) to unreadable (ciphertext) form and vice-versa. The AES has also been employed in other areas such as to secure information in smart cards and online transactions. This year marks the fifth year that the AES has been adopted as a standard. During that period, many attacks have been performed on the cipher. However, none have fully broken the complete round cipher. All of the attacks were launched on reduced-round version and the complexity is compared to that of brute force. Brute force is an attack that tries every possible value of the key of the cipher. Therefore, it serves as the upper bound on the attack on block ciphers. In this paper, we will review some existing cryptanalytic attacks on AE

Enhancement of Non-Permutation Binomial Power Functions to Construct Cryptographically Strong S-Boxes

A Substitution box (S-box) is an important component used in symmetric key cryptosystems to satisfy Shannon’s property on confusion. As the only nonlinear operation, the S-box must be cryptographically strong to thwart any cryptanalysis tools on cryptosystems. Generally, the S-boxes can be constructed using any of the following approaches: the random search approach, heuristic/evolutionary approach or mathematical approach. However, the current S-box construction has some drawbacks, such as low cryptographic properties for the random search approach and the fact that it is hard to develop mathematical functions that can be used to construct a cryptographically strong S-box. In this paper, we explore the non-permutation function that was generated from the binomial operation of the power function to construct a cryptographically strong S-box. By adopting the method called the Redundancy Removal Algorithm, we propose some enhancement in the algorithm such that the desired result can be obtained. The analytical results of our experiment indicate that all criteria such as bijective, nonlinearity, differential uniformity, algebraic degree and linear approximation are found to hold in the obtained S-boxes. Our proposed S-box also surpassed several bijective S-boxes available in the literature in terms of cryptographic properties

I-PRESENT TM: An Involutive Lightweight Block Cipher

This paper proposes a new involutive light-weight block cipher for resource-constraint environments called I-PRESENT TM. The design is based on the Present block cipher which is included in the ISO/IEC 29192 standard on lightweight cryptography. The advantage of I-PRESENT TM is that the cipher is involutive such that the encryption circuit is identical to decryption. This is an advantage for environments which require the implementation of both circuits. The area requirement of I-PRESENT TM compares reasonably well with other similar ciphers such as PRINCE

Machine Learning-Based Cooperative Spectrum Sensing in Dynamic Segmentation Enabled Cognitive Radio Vehicular Network

A vehicle ad hoc network (VANET) is a solution for road safety, congestion management, and infotainment services. Integration of cognitive radio (CR), known as CR-VANET, is needed to solve the spectrum scarcity problems of VANET. Several research efforts have addressed the concerns of CR-VANET. However, more reliable, robust, and faster spectrum sensing is still a challenge. A novel segment-based CR-VANET (Seg-CR-VANET) architecture is therefore proposed in this paper. Roads are divided equally into segments, and they are sub-segmented based on the probability value. Individual vehicles or secondary users produce local sensing results by choosing an optimal spectrum sensing (SS) technique using a hybrid machine learning algorithm that includes fuzzy and naïve Bayes algorithms. We used dynamic threshold values for the sensing techniques. In this proposed cooperative SS, the segment spectrum agent (SSA) made the global decision using the tri-agent reinforcement learning (TA-RL) algorithm. Three environments (network, signal, and vehicle) are learned by this proposed algorithm to determine primary (licensed) users’ activities. The simulation results indicate that, compared to current works, the proposed Seg-CR-VANET produces better results in spectrum sensing