Block Cipher Research Articles

Improved integral neural distinguisher model for lightweight cipher PRESENT

PRESENT is a lightweight block cipher, which has attracted many scholars to research its security. In 2022, Zahednejad et al. proposed the integral neural distinguisher on round-reduced PRESENT. In this paper, a new integral neural distinguisher for PRESENT is constructed. In contrast to Zahednejad’s works, the proposed integral neural distinguisher can improve the number of attack rounds in one round. This paper proposes a new data format (invP0n,invP1n,…,invP15n,invS0n,invS1n,…,invS15n), which can exposes more features of PRESENT previous round ciphertext. Simultaneously, this paper incorporates MBConv module into the convolutional layers of DenseNet, which enable the neural network to identify a greater variety of features in ciphertext. The data format of the paper is analysed. The results of the analysis show that the data format in this paper is able to identify more features compared to Zahednejad’s data format. Further to this, experiments are performed on PRESENT using the integral neural distinguisher in this paper. The experimental results show that the changes make to the neural network and data format have improved the accuracy of distinguishers. Finally, key recovery attacks are conducted on the integral neural distinguishers of SmallPresent-(8) to demonstrate the efficacy of the distinguisher proposed in this paper. The results demonstrate that the key recovery success rates for 5-round and 6-round are 98% and 90%, considering error bits within a range of two bits.

Improving the Security of the LCB Block Cipher Against Deep Learning-Based Attacks

This study presents a robust version of Light Cipher Block (LCB) by addressing the vulnerabilities identified in previous versions. The vulnerabilities in LCB, including a linear S-Box, improper bit shuffling, and subkey reusability, were thoroughly examined. To overcome these weaknesses, a modified version called Secure LCB is introduced, incorporating changes to the P-Box and key generation algorithm. Motivated by Gohr’s work at CRYPTO’19, this paper investigates the use of a neural distinguisher built upon a 1-dimensional convolutional neural network (1-d CNN). The deep learning model is tasked with identifying ciphertexts that have a specific, controlled difference in their inputs, as opposed to those with random input differences. The evaluation of the proposed Secure LCB using the neural distinguisher suggests that the modifications made to LCB have effectively enhanced its resistance against the neural distinguisher’s classification. This highlights the importance of addressing vulnerabilities in cryptographic systems and showcases the potential of machine learning techniques in cryptanalysis.

ADS-B Communication Security: Assessing the Performance of Format-Preserving Lightweight Block Ciphers

In this work, we document an investigation regarding the vulnerabilities of ADS-B (automatic dependent surveillance-broadcast) communication, considering the risks to operational security. The ADS-B technology is a communication protocol to transmit surveillance information, that is, data related to position, altimetry, and speed, from the aircraft's avionics, an evolution to radars. We present an encryption solution for this communication using format-preserving encryption implemented in a microcontroller-embedded system. We also evaluate the use of lightweight symmetric block ciphers for better computational performance. When used as a pseudorandom function, we observe that such ciphers maintain a high entropy output value with low computational cost --- up to sixteen times faster for similar entropy values. Finally, the computational performance obtained with the proposed solution is analyzed by processing real-time data from aircraft in the landing and takeoff phase at the international airport of Recife/Guararapes - Gilberto Freyre, based in Recife/PE, Brazil.

Quantum differential cryptanalysis based on Bernstein-Vazirani algorithm

Recent research has demonstrated the potential of quantum algorithms to exploit vulnerabilities in various popular constructions, such as certain block ciphers like Feistel, Even-Mansour, and multiple MACs, within the superposition query model. In this study, we delve into the security of block ciphers against quantum threats, particularly investigating their susceptibility to cryptanalysis techniques, notably exploring quantum adaptations of differential cryptanalysis. Initially, we introduce a BV-based quantum algorithm for identifying linear structures with a complexity of O(n), where n denotes the number of bits in the function. Subsequently, we illustrate the application of this algorithm in devising quantum differential cryptanalysis techniques, including quantum differential cryptanalysis, quantum small probability differential cryptanalysis, and quantum impossible differential cryptanalysis, demonstrating polynomial acceleration compared to prior approaches. By treating the encryption function as a unified entity, our algorithm circumvents the traditional challenge of extending differential paths in differential cryptanalysis.

Enhanced related-key differential neural distinguishers for SIMON and SIMECK block ciphers.

At CRYPTO 2019, Gohr pioneered the application of deep learning to differential cryptanalysis and successfully attacked the 11-round NSA block cipher Speck32/64 with a 7-round and an 8-round single-key differential neural distinguisher. Subsequently, Lu et al. (DOI 10.1093/comjnl/bxac195) presented the improved related-key differential neural distinguishers against the SIMON and SIMECK. Following this work, we provide a framework to construct the enhanced related-key differential neural distinguisher for SIMON and SIMECK. In order to select input differences efficiently, we introduce a method that leverages weighted bias scores to approximate the suitability of various input differences. Building on the principles of the basic related-key differential neural distinguisher, we further propose an improved scheme to construct the enhanced related-key differential neural distinguisher by utilizing two input differences, and obtain superior accuracy than Lu et al. for both SIMON and SIMECK. Specifically, our meticulous selection of input differences yields significant accuracy improvements of 3% and 1.9% for the 12-round and 13-round basic related-key differential neural distinguishers of SIMON32/64. Moreover, our enhanced related-key differential neural distinguishers surpass the basic related-key differential neural distinguishers. For 13-round SIMON32/64, 13-round SIMON48/96, and 14-round SIMON64/128, the accuracy of their related-key differential neural distinguishers increases from 0.545, 0.650, and 0.580 to 0.567, 0.696, and 0.618, respectively. For 15-round SIMECK32/64, 19-round SIMECK48/96, and 22-round SIMECK64/128, the accuracy of their neural distinguishers is improved from 0.547, 0.516, and 0.519 to 0.568, 0.523, and 0.526, respectively.

Enhancing Cloud Security: Strategies and Technologies for Protecting Data in Cloud Environments

With the rapid adoption of cloud computing, ensuring the security of data has become a paramount concern for organizations of all sizes. This paper explores various strategies and technologies for protecting data in cloud environments. Encryption emerges as a fundamental technique for safeguarding data both at rest and in transit, with block ciphers and stream ciphers offering robust encryption methods. Additionally, hash functions play a critical role in verifying data integrity and ensuring secure data storage. We delve into the implementation of these techniques in cloud environments, highlighting best practices for encryption, key management, and data integrity verification. Moreover, we address common cloud vulnerabilities, such as lack of appropriate governance, isolation failure, and malicious attacks, and discuss mitigation strategies to strengthen cloud security posture. By leveraging encryption, block ciphers, stream ciphers, and hash functions, organizations can enhance data protection in the cloud and mitigate the risks associated with unauthorized access, data breaches, and tampering. This paper serves as a comprehensive guide for organizations seeking to enhance their cloud security and maintain trust in cloud-based services.

A new S-box pattern generation based on chaotic enhanced logistic map: case of 5-bit S-box

Cryptography plays consistently an essential role in securing any sort of communications or data broadcast over the network. Since the security of any designed block cipher algorithm is related to its Substitution box (S-box), several efforts have been made by researchers to design a vigorous S-box that maintains flawlessly the cost, performance, and security trade-off. From the literature, we can find a variety of input–output sizes of S-boxes, each of which has its benefits and drawbacks. Therefore, this work introduces a new S-box pattern generation based on the chaotic enhanced logistic map where its chaotic behavior offers good randomness ability, a fact that enhances its unpredictability. In our realization, the intention was the generation of a 5-bit Sbox due to its suitability and cost-effectiveness to be integrated into lightweight cryptosystems. Moreover, the S-box security strength is proved by testing it through numerous cryptanalysis measurements. We can mention non-linearity, bijectivity, linearity, differential cryptanalysis, boomerang attacks resilience, Avalanche effect, and algebraic attacks resilience. The results show that our proposition provides good resistance to the aforementioned attacks and even shows superiority over its 5-bit competitors in terms of non-linearity, differential, Boomerang, and algebraic attack resistance.

Text Data Security Using LCG and CBC with Steganography Technique on Digital Image

This research proposes a text data security method using a combination of Linear Congruential Generator (LCG), Advanced Encryption Standard (AES) Cipher Block Chaining (CBC) mode, and Least Significant Bit (LSB) steganography technique on digital images. The message scrambling process using LCG produces ASCII characters as noise that is inserted in the original message. After that, the message is encrypted using AES-256 CBC to provide additional security. The encryption result is then hidden in the digital image through LSB steganography technique. Tests were conducted on images with JPEG and BMP formats to measure the visual quality after the data insertion process, as measured by PSNR (Peak Signal-to-Noise Ratio). The test results show a PSNR value of 56.60 dB for JPEG images and 70.84 dB for BMP images. In addition, the insertion process in JPEG images degrades the image quality, mainly due to lossy compression, compared to the lossless BMP format. This study concludes that the proposed combination of methods is effective in hiding messages in images, but is susceptible to compression on lossy formats such as JPEG. The use of lossless image formats such as BMP or PNG is recommended to maintain data integrity.

One Formalized Approach to Truncated Differential Cryptanalysis of Block Ciphers

Abstract We propose a formalized approach to truncated differential cryptanalysis based on ternary masks that separately account for unchanged, obligatorily changed, and unknown bits in differences. We introduce a security parameter for S-boxes and encryption mappings, which bounds the probability of truncated differentials from below, and examine its basic algebraic properties. Our approach enables the adaptation of existing techniques used in classical differential attacks to truncated differential cryptanalysis, allowing us to extract more information from the encryption process and evaluate the complexity of truncated differential attacks.

Balancing Security and Efficiency: A Power Consumption Analysis of a Lightweight Block Cipher

This research paper presents a detailed analysis of a lightweight block cipher’s (LWBC) power consumption and security features, specifically designed for IoT applications. To accurately measure energy consumption during the execution of the LWBC algorithm, we utilised the Qoitech Otii Arc, a specialised tool for optimising energy usage. Our experimental setup involved using the Otii Arc as a power source for an Arduino NodeMCU V3, running the LWBC security algorithm. Our methodology focused on energy consumption analysis using the shunt resistor technique. Our findings reveal that the LWBC is highly efficient and provides an effective solution for energy-limited IoT devices. We also conducted a comparative analysis of the proposed cipher against established LWBCs, which demonstrated its superior performance in terms of energy consumption per bit. The proposed LWBC was evaluated based on various key dimensions such as power efficiency, key and block size, rounds, cipher architecture, gate area, ROM, latency, and throughput. The results of our analysis indicate that the proposed LWBC is a promising cryptographic solution for energy-conscious and resource-limited IoT applications.

Generating of A Dynamic and Secure S-Box for AES Block Cipher System Based on Modified Hexadecimal Playfair Cipher

In today's digital world, the extensive use of devices, including smartphones, tablets, IoT devices, and the internet, emphasizes the necessity for strong security measures. These measures are essential to safeguard both user data and sensitive government information. As technology advances, the enhancement of cryptographic methods becomes imperative, ensuring alignment with this rapid progression. This paper introduces a novel approach to encrypting classified data using the modified AES cipher system. It employs a dynamic and secure substitution byte operation with a 4×4 matrix, replacing the static and public 16×16 S-box found in the original version of AES. To construct the presented S-box, the system makes a secret key of 56 bytes (4×14 Rounds), where each round of the AES-256 utilizes 4 bytes to generate a 4×4 Hexadecimal Playfair matrix. By altering the S-boxes in each round and block (where each block utilizes AES key expansion to generate a 56-byte secret key for the Playfair matrix), it becomes feasible to produce distinct encrypted blocks even when the original blocks remain identical. In terms of security, the effectiveness of the provided method has been verified by experimental and analytical studies including the Avalanche effect, Balanced output, Hamming distance, and Time complexity. The result shows that the Avalanche effect of the proposed method exceeds 50% and increase the time of brute force attack. Moreover, the proposed algorithm exhibits impressive execution speed, handling approximately 200 KB in just one second.

Developing a Graphical Domain-Specific Modeling Language for Efficient Lightweight Block Cipher Schemas Configuration: LWBCLang

The lightweight block cipher is an encryption technique with negligible computational overhead. Despite its advantages, it faces a substantial challenge. Correct handwriting of the script code for the cipher scheme is a challenge for programmers. In this research, we suggest a new graphical domain-specific modeling language to make it easier for both non-technical users and domain specialists to implement lightweight block cipher schemes. The proposed language, called LWBCLang, is a modular and extensible language that offers graphical components for constructing three essential types of inner block cipher structures. Seven different methods of keystream generation and all the tests of the NIST suite with performance analysis are provided. In the context of its meta-model, LWBCLang's abstract and concrete syntaxes are specified. LWBCLang has been implemented as an internal DSML with Python as the host language. The evaluation of LWBCLang is based on qualitative analysis to demonstrate the language's effectiveness and efficiency. Further benefits of this proposed language are evaluated and discussed in depth in this research.

Next-Generation Block Ciphers: Achieving Superior Memory Efficiency and Cryptographic Robustness for IoT Devices

Traditional cryptographic methods often need complex designs that require substantial memory and battery power, rendering them unsuitable for small handheld devices. As the prevalence of these devices continues to rise, there is a pressing need to develop smart, memory-efficient cryptographic protocols that provide both high speed and robust security. Current solutions, primarily dependent on dynamic permutations, fall short in terms of encryption and decryption speeds, the cryptographic strength, and the memory efficiency. Consequently, the evolution of lightweight cryptographic algorithms incorporating randomised substitution properties is imperative to meet the stringent security demands of handheld devices effectively. In this paper, we present an advanced design of lightweight block ciphers that enhances traditional dynamic permutations with innovative randomised substitutions. This design utilises straightforward randomized encryption methods such as XOR, nibble swap, count ones, and left shift. The cryptographic robustness of our proposed block cipher has been rigorously tested through several standardised statistical tests, as recommended by the National Institute of Standards and Technology (NIST). These evaluations confirm that our algorithm maintains strong cryptographic properties with randomised substitutions and outperforms existing models in several key aspects. Moreover, comparative assessments reveal that our algorithm achieves a throughput of 853.31 Kbps while consuming only 1510 bytes of memory and demonstrating over 60% avalanche properties, significantly outperforming other solutions in terms of CPU utilisation and memory consumption. These results underscore the efficacy of our approach in fulfilling the advanced security requirements of modern handheld devices.

Hybridized data encoding based encryption and Diffie Hellman decryption for security enhancement

Information security is extremely important because the modification and interception of the data can lead to loss of data integrity, confidentiality, and availability. The encryption process is a critical component of classic standard encryption techniques such as advanced encryption, standard block ciphers, and data encryption standards. The traditional algorithm requires increased power consumption and memory. Therefore, there is a need to develop an efficient cryptographic operation to promote better security. The encryption process is performed using the Hybridized Data Encoding (HDE) model. Here, the optimal keys can be selected using the Chaotic Coati optimization algorithm (ChCoA). The decryption process can be performed using the Enhanced Diffie Hellman algorithm (EDHA). The resulting achievements will include effective encryption and decryption algorithms that together strengthen data security, facilitate secure data exchange, and provide superior protection against data vulnerabilities. By implementing the proposed work in the Matlab working platform, the performances in terms of encryption time, decryption time, processing time and throughput are analyzed. The proposed model achieved a throughput of 249.1093 (kb/s) and a processing time of 0.55 (sec).

An Image Encryption Method Based on a High-performance and Efficient Block Cipher

An Image Encryption Method Based on a High-performance and Efficient Block Cipher

Truncated Differential-Neural Key Recovery Attacks on Round-Reduced HIGHT

Recently, differential-neural cryptanalysis, which integrates deep learning with differential cryptanalysis, has emerged as a powerful and practical cryptanalysis method. It has been particularly applied to lightweight block ciphers, which are characterized by simple structures and operations, and relatively small block and key sizes. In resource-constrained environments, such as Internet of Things (IoT), it is essential to verify the resistance of existing lightweight block ciphers against differential-neural cryptanalysis to ensure security. In differential-neural cryptanalysis, a deep learning model, known as a neural distinguisher, is trained to differentiate a target cipher from others, facilitating key recovery through statistical analysis. For successful differential-neural cryptanalysis, it is crucial to develop a highly accurate neural distinguisher and to optimize the key recovery attack algorithm. In this paper, we introduce a novel neural distinguisher and key recovery attack against the 15-round reduced HIGHT cipher. Our proposed neural distinguisher is capable of distinguishing HIGHT ciphertext by analyzing only a portion of the ciphertext, which we refer to as a truncated neural distinguisher. Notably, our experiments demonstrate that the truncated neural distinguisher achieves performance comparable to existing distinguishers trained on entire ciphertext blocks, while enabling a more efficient key recovery attack through a divide-and-conquer strategy. Furthermore, we observe a significant improvement in key recovery efficiency compared to traditional cryptanalysis methods.

DLSEA-64: dynamic lightweight symmetric encryption algorithm for secure data transmission in IoT based pervasive sensing applications

ABSTRACT Remote patient monitoring, particularly for the elderly, is gaining popularity due to the Internet of Medical Things (IoMT). However, IoMT devices are highly vulnerable to cyber attacks related to data confidentiality. Hence, lightweight cryptographic algorithms are found to be highly necessary to ensure safe data transfer in such devices. This paper introduces a Dynamic Lightweight Symmetric Encryption Algorithm (DLSEA) in order to challenge issues related to real-time data security and privacy. DLSEA is executed on Arduino Uno (ATmega328P) and Arduino Mega (ATmega2560) microcontrollers by using the Proteus simulation tool that employs a 64-bit temporal secret key and a 64-bit block cipher for encryption of data. Several experiments were conducted using payloads of varying sizes to record execution time, energy consumption, RAM usage, ROM usage and avalanche effect. The experiments demonstrated that for data sizes between 128-512 bytes, DLSEA requires 2.49-3.27 ms on the ATmega2560, which is 47% faster than a similar algorithm known as PRESENT. Additionally, DLSEA is 46% more energy-efficient, with RAM usage decreased by 41.81% and ROM usage decreased by 57.19% compared to PRESENT. Experimental results also show that DLSEA satisfies the plaintext sensitivity test, the key sensitivity test, and the rigorous avalanche criteria test (i.e., ~50%).

Combined and General Methodologies of Key Space Partition for the Cryptanalysis of Block Ciphers

This paper proposes two new methods of key space partitioning for the cryptanalysis of block ciphers. The first one is called combined methodology of key space partition (CoMeKSPar), which allows us to simultaneously set some of the first and last consecutive bits of the key. In this way, the search is performed using the remaining middle bits. CoMeKSPar is a combination of two methods already proposed in the scientific literature, the Borges, Borges, Monier (BBM) and the Tito, Borges, Borges (TBB). The second method is called the general algorithm of key space reduction (GAKSRed), which makes it possible to perform a genetic algorithm search in the space formed by the unknown bits of the key, regardless of their distribution in the binary block. Furthermore, a method of attacking block ciphers is presented for the case where some key bits are known; the basic idea is to deduce some of the remaining bits of the block. An advantage of these methods is that they allow parallel computing, which allows simultaneous searches in different sub-blocks of key bits, thereby increasing the probability of success. The experiments are performed with the KLEIN (Small) lightweight block cipher using the genetic algorithm.

Some Constructions of Perfect c-Nonlinear and Pseudo-Perfect c-Nonlinear Functions

The perfect nonlinear functions play an important role in block ciphers and have been widely investigated in the literature. Subsequently, the perfect [Formula: see text]-nonlinear functions were generalized by Ellingsen et al. in 2020. In this paper, we study the [Formula: see text]-differential uniformity of some functions. We first construct some perfect [Formula: see text]-nonlinear functions from known ones. Additionally, pseudo-perfect [Formula: see text]-nonlinear functions are also investigated and a necessary and sufficient condition for a function to be pseudo-perfect [Formula: see text]-nonlinear is presented. Meanwhile, some pseudo-perfect [Formula: see text]-nonlinear functions are constructed. Remarkably, we completely give the difference distribution table of a function with respect to the pseudo [Formula: see text]-derivative.

Tweakable ForkCipher from Ideal Block Cipher

In ASIACRYPT 2019, Andreeva et al. introduced a new symmetric key primitive called the forkcipher, designed for lightweight applications handling short messages. A forkcipher is a keyed function with a public tweak, featuring fixed-length input and fixed-length (expanding) output. They also proposed a specific forkcipher, ForkSkinny, based on the tweakable block cipher SKINNY, and its security was evaluated through cryptanalysis. Since then, several efficient AEAD and MAC schemes based on forkciphers have been proposed, catering not only to short messages but also to various purposes such as leakage resilience and cloud security. While forkciphers have proven to be efficient solutions for designing AEAD schemes, the area of forkcipher design remains unexplored, particularly the lack of provably secure forkcipher constructions. In this work, we propose forkcipher design for various tweak lengths, based on a block cipher as the underlying primitive. We provide proofs of security for these constructions, assuming the underlying block cipher behaves as an ideal block cipher. First, we present a forkcipher, F ~ 1 , for an n -bit tweak and prove its optimal ( n -bit) security. Next, we propose another construction, F ~ 2 , for a 2 n -bit tweak, also proving its optimal ( n -bit) security. Finally, we introduce a construction, F ~ r , for a general r n -bit tweak, achieving n -bit security.