A Review on Biological Inspired Computation in Cryptology

Cryptology is a field that concerned with cryptography and cryptanalysis. Cryptography, which is a key technology in providing a secure transmission of information, is a study of designing strong cryptographic algorithms, while cryptanalysis is a study of breaking the cipher. Recently biological approaches provide inspiration in solving problems from various fields. This paper reviews major works in the application of biological inspired computational (BIC) paradigm in cryptology. The paper focuses on three BIC approaches, namely, genetic algorithm (GA), artificial neural network (ANN) and artificial immune system (AIS). The findings show that the research on applications of biological approaches in cryptology is minimal as compared to other fields. To date only ANN and GA have been used in cryptanalysis and design of cryptographic primitives and protocols. Based on similarities that AIS has with ANN and GA, this paper provides insights for potential application of AIS in cryptology for further research

A survey on machine learning applied to symmetric cryptanalysis

In this work we give a short review of the recent progresses of machine learning techniques applied to cryptanalysis of symmetric ciphers, with particular focus on artificial neural networks. We start with some terminology and basics of neural networks, to then classify the recent works in two categories: "black-box cryptanalysis", techniques that not require previous information about the cipher, and "neuro-aided cryptanalysis", techniques used to improve existing methods in cryptanalysis

Cryptography: Against AI and QAI Odds

Artificial Intelligence (AI) presents prodigious technological prospects for development, however, all that glitters is not gold! The cyber-world faces the worst nightmare with the advent of AI and quantum computers. Together with Quantum Artificial Intelligence (QAI), they pose a catastrophic threat to modern cryptography. It would also increase the capability of cryptanalysts manifold, with its built-in persistent and extensive predictive intelligence. This prediction ability incapacitates the constrained message space in device cryptography. With the comparison of these assumptions and the intercepted ciphertext, the code-cracking process will considerably accelerate. Before the vigorous and robust developments in AI, we have never faced and never had to prepare for such a plaintext-originating attack. The supremacy of AI can be challenged by creating ciphertexts that would give the AI attacker erroneous responses stymied by randomness and misdirect them. AI threat is deterred by deviating from the conventional use of small, known-size keys and pattern-loaded ciphers. The strategy is vested in implementing larger secret size keys, supplemented by ad-hoc unilateral randomness of unbound limitations and a pattern-devoid technique. The very large key size can be handled with low processing and computational burden to achieve desired unicity distances. The strategy against AI odds is feasible by implementing non-algorithmic randomness, large and inexpensive memory chips, and wide-area communication networks. The strength of AI, i.e., randomness and pattern detection can be used to generate highly optimized ciphers and algorithms. These pattern-devoid, randomness-rich ciphers also provide a timely and plausible solution for NIST's proactive approach toward the quantum challenge

A Deep Neural Differential Distinguisher for ARX based Block Cipher

Over the last few years, deep learning is becoming the most trending topic for the classical cryptanalysis of block ciphers. Differential cryptanalysis is one of the primary and potent attacks on block ciphers. Here we apply deep learning techniques to model differential cryptanaly- sis more easily. In this paper, we report a generic tool called NDDT1, us- ing deep neural classifier that assists to find differential distinguishers for symmetric block ciphers with reduced round. We apply this approach for the differential cryptanalysis of ARX-based encryption schemes HIGHT, LEA, SPARX and SAND. To the best of our knowledge, this is the first deep learning-based distinguisher for the mentioned ciphers. The result shows that our deep learning based distinguishers work with high accuracy for 14-round HIGHT, 13-Round LEA, 11-round SPARX and 14-round SAND128. The relationship between the hamming weight of input difference of a neural distinguisher and the corresponding maxi- mum round number of the cipher has been justified through exhaustive experimentation. The lower bounds of data complexity for differential cryptanalysis have also been improved

Deep Learning based Cryptanalysis of Stream Ciphers

Conventional cryptanalysis techniques necessitate an extensive analysis of non-linear functions defining the relationship of plain data, key, and corresponding cipher data. These functions have very high degree terms and make cryptanalysis work extremely difficult. The advent of deep learning algorithms along with the better and efficient computing resources has brought new opportunities to analyze cipher data in its raw form. The basic principle of designing a cipher is to introduce randomness into it, which means the absence of any patterns in cipher data. Due to this fact, the analysis of cipher data in its raw form becomes essential. Deep learning algorithms are different from conventional machine learning algorithms as the former directly work on raw data without any formal requirement of feature selection or feature extraction steps. With these facts and the assumption of the suitability of employing deep learning algorithms for cipher data, authors introduced a deep learning based method for finding biases in stream ciphers in the black-box analysis model. The proposed method has the objective to predict the occurrence of an output bit/byte at a specific location in the stream cipher generated keystream. The authors validate their method on stream cipher RC4 and its improved variant RC4A and discuss the results in detail. Further, the authors apply the method on two more stream ciphers namely Trivium and TRIAD. The proposed method can find bias in RC4 and shows the absence of this bias in its improved variant and other two ciphers. Focusing on RC4, the authors present a comparative analysis with some existing methods in terms of approach and observations and showed that their process is more straightforward and less complicated than the existing ones

An overview of memristive cryptography

Smaller, smarter and faster edge devices in the Internet of things era demands secure data analysis and transmission under resource constraints of hardware architecture. Lightweight cryptography on edge hardware is an emerging topic that is essential to ensure data security in near-sensor computing systems such as mobiles, drones, smart cameras, and wearables. In this article, the current state of memristive cryptography is placed in the context of lightweight hardware cryptography. The paper provides a brief overview of the traditional hardware lightweight cryptography and cryptanalysis approaches. The contrast for memristive cryptography with respect to traditional approaches is evident through this article, and need to develop a more concrete approach to developing memristive cryptanalysis to test memristive cryptographic approaches is highlighted.Comment: European Physical Journal: Special Topics, Special Issue on "Memristor-based systems: Nonlinearity, dynamics and applicatio

Distinguishing Lightweight Block Ciphers in Encrypted Images

Modern day lightweight block ciphers provide powerful encryption methods for securing IoT communication data. Tiny digital devices exchange private data which the individual users might not be willing to get disclosed. On the other hand, the adversaries try their level best to capture this private data. The first step towards this is to identify the encryption scheme. This work is an effort to construct a distinguisher to identify the cipher used in encrypting the traffic data. We try to establish a deep learning based method to identify the encryption scheme used from a set of three lightweight block ciphers viz. LBlock, PRESENT and SPECK. We make use of images from MNIST and fashion MNIST data sets for establishing the cryptographic distinguisher. Our results show that the overall classification accuracy depends firstly on the type of key used in encryption and secondly on how frequently the pixel values change in original input image