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This is our first IET Information Security special issue from the Internation Conference on Information Security and Cryptology, which was held from December 1 to December 3, Seoul, South Korea, 2021. TOPIC B Paper 3 by K. Yamamura, Y. Wang, and E. Fujisaki investigates an enumeration algorithm that is used as a subroutine for the BKZ algorithm, which is one of the most practical reduction algorithms. [Extracted from the article]

The widespread adoption of Internet of Things (IoT) devices in home, industrial, and business environments has made available the deployment of innovative distributed measurement systems (DMS). This paper takes into account constrained hardware and a security-oriented virtual local area network (VLAN) approach that utilizes local message queuing telemetry transport (MQTT) brokers, transport layer security (TLS) tunnels for local sensor data, and secure socket layer (SSL) tunnels to transmit TLS-encrypted data to a cloud-based central broker. On the other hand, the recent literature has shown a correlated exponential increase in cyber attacks, mainly devoted to destroying critical infrastructure and creating hazards or retrieving sensitive data about individuals, industrial or business companies, and many other entities. Much progress has been made to develop security protocols and guarantee quality of service (QoS), but they are prone to reducing the network throughput. From a measurement science perspective, lower throughput can lead to a reduced frequency with which the phenomena can be observed, generating, again, misevaluation. This paper does not give a new approach to protect measurement data but tests the network performance of the typically used ones that can run on constrained hardware. This is a more general scenario typical for IoT-based DMS. The proposal takes into account a security-oriented VLAN approach for hardware-constrained solutions. Since it is a worst-case scenario, this permits the generalization of the achieved results. In particular, in the paper, all OpenSSL cipher suites are considered for compatibility with the Mosquitto server. The most used key metrics are evaluated for each cipher suite and QoS level, such as the total ratio, total runtime, average runtime, message time, average bandwidth, and total bandwidth. Numerical and experimental results confirm the proposal's effectiveness in foreseeing the minimum network throughput concerning the selected QoS and security. Operating systems yield diverse performance metric values based on various configurations. The primary objective is identifying algorithms to ensure suitable data transmission and encryption ratios. Another aim is to explore algorithms that ensure wider compatibility with existing infrastructures supporting MQTT technology, facilitating secure connections for geographically dispersed DMS IoT networks, particularly in challenging environments like suburban or rural areas. Additionally, leveraging open firmware on constrained devices compatible with various MQTT protocols enables the customization of the software components, a crucial necessity for DMS. [ABSTRACT FROM AUTHOR]

Data outsourcing has become more and more popular due to its low cost and flexibility. However, there is a problem that the cloud server used to store data is partially trusted. Searchable encryption is an efficient technology that is devoted to helping people conduct accurate searches without leaking information. Nonetheless, most existing schemes cannot support dynamic updates or meet the privacy requirements of all users. There have been some experiments to solve these issues by implementing a dynamically searchable asymmetric encryption scheme. This paper proposes an efficient searchable encryption scheme based on the Authenticator Bloom Filter (ABF). The solution can support dynamic updates and multiple users and meet forward and backward security. This paper uses an ABF to improve the efficiency of searches and updates while playing a significant role in dynamic updates. This paper designs a new token encryption scheme and file set encryption scheme, which not only helps users reduce time in searches and updates but also supports multi-user modes. Experiments show that the proposed scheme takes less time in searching and updating algorithms, especially when the keyword does not exist. The solution also takes into account the problem of history storage when updating, which reduces the unnecessary consumption of memory and avoids multiple storage states for the same file. [ABSTRACT FROM AUTHOR]

In the realm of smart communication systems, where the ubiquity of 5G/6G networks and IoT applications demands robust data confidentiality, the cryptographic integrity of block and stream cipher mechanisms plays a pivotal role. This paper focuses on the enhancement of cryptographic strength in these systems through an innovative approach to generating substitution boxes (S-boxes), which are integral in achieving confusion and diffusion properties in substitution–permutation networks. These properties are critical in thwarting statistical, differential, linear, and other forms of cryptanalysis, and are equally vital in pseudorandom number generation and cryptographic hashing algorithms. The paper addresses the challenge of rapidly producing random S-boxes with desired cryptographic attributes, a task notably arduous given the complexity of existing generation algorithms. We delve into the hill climbing algorithm, exploring various cost functions and their impact on computational complexity for generating S-boxes with a target nonlinearity of 104. Our contribution lies in proposing a new cost function that markedly reduces the generation complexity, bringing down the iteration count to under 50,000 for achieving the desired S-box. This advancement is particularly significant in the context of smart communication environments, where the balance between security and performance is paramount. [ABSTRACT FROM AUTHOR]

Polynomial multiplication is the most computationally expensive part of the lattice-based cryptography algorithm. However, the existing acceleration schemes have problems, such as low performance and high hardware resource overhead. Based on the polynomial multiplication of number theoretic transformation (NTT), this paper proposed a simple element of Montgomery module reduction with pipeline structure to realize fast module multiplication. In order to improve the throughput of the NTT module, the block storage technology is used in the NTT hardware module to enable the computing unit to read and write data alternately. Based on the NTT hardware module, a precalculated parameter storage and real-time calculation method suitable for the hardware architecture of this paper is also proposed. Finally, the hardware of polynomial multiplier based on NTT module is implemented, and its function simulation and performance evaluation are carried out. The results show that the proposed hardware accelerator can have excellent computing performance while using fewer hardware resources, thus meeting the requirements of lattice cipher algorithms in security chips. Compared with the existing studies, the computing performance of the polynomial multiplier designed in this paper is improved by approximately 1 to 3 times, and the slice resources and storage resources used are reduced by approximately 60% and 17%, respectively. [ABSTRACT FROM AUTHOR]

Communication security has become particularly crucial with the rapid development of the Internet of Things (IoT). Frequency hopping spread spectrum (FHSS) technology, a prevalent method in wireless communication, has a wide range of applications in the Internet of Things. Enhancing the security of frequency hopping sequences is an essential means to improve the security of frequency hopping communication in the Internet of Things, as the performance of frequency hopping sequences plays a crucial role in frequency hopping systems. This paper proposes constructing secure adaptive frequency hopping sequence sets based on the advanced encryption standard (AES) algorithm. As a block cipher algorithm with superior security, the AES algorithm can provide a fundamental guarantee for the security of the proposed frequency hopping sequences. The mapping methods from ciphertext sequences to frequency hopping sequences proposed in this paper can achieve the construction of frequency hopping sequences of any frequency set size to meet the requirements of adaptive frequency hopping. In addition, we also model and analyse the problem of overlapping spectrum band of the IoT groups in the industrial, scientific, and medical (ISM) band, aiming to achieve better packet transmission performance by adjusting the frequency set size. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence (AI), deep learning is widely used in various industries. At CRYPTO 2019, researchers used deep learning to analyze the block cipher for the first time and constructed a differential neural network distinguisher to meet a certain accuracy. In this paper, a mixture differential neural network distinguisher using ResNet is proposed to further improve the accuracy by exploring the mixture differential properties. Experiments are conducted on SIMON32/64, and the accuracy of the 8-round mixture differential neural network distinguisher is improved from 74.7% to 92.3%, compared with that of the previous differential neural network distinguisher. The prediction accuracy of the differential neural network distinguisher is susceptible to the choice of the specified input differentials, whereas the mixture differential neural network distinguisher is less affected by the input difference and has greater robustness. Furthermore, by combining the probabilistic expansion of rounds and the neutral bit, the obtained mixture differential neural network distinguisher is extended to 11 rounds, which can realize the 12-round actual key recovery attack on SIMON32/64. With an appropriate increase in the time complexity and data complexity, the key recovery accuracy of the mixture differential neural network distinguisher can be improved to 55% as compared to 52% of the differential neural network distinguisher. The mixture differential neural network distinguisher proposed in this paper can also be applied to other lightweight block ciphers. [ABSTRACT FROM AUTHOR]

In this paper, we present some new observations on the branch number and study concrete differential analysis of SPEEDY. It is a new low-latency block cipher proposed at TCHES 2021. It employs SPS-type round function and consists of only 5/6/7 rounds. Since the iteration rounds are rather small so as to achieve ultra low-latency in encryption speed, it will be crucially important to analyze its security margin accurately. In this paper, we first propose a new notation of partition branch number which can describe the minimum number of active S-boxes for 2-round SPEEDY more accurately. An efficient algorithm to compute the value of partition branch number is also given. Then by extending the notation to higher-order partition branch number, we can obtain more accurate results of the minimum number of active S-boxes for 3–7 rounds. As a result, the maximum expected differential probabilities are significantly higher than the results estimated by designers. Based on this, we search for optimal differential characteristics of SPEEDY while considering the difference distribution table of S-box. We present examples of differential characteristics for 2–7 rounds. Furthermore, by utilizing the simple bit-permutation key schedule of SPEEDY, we can extend the differential trail search method and construct an efficient 6-round related-key differential trail with probability 2 - 179.2 . Based on it, we can present related-key differential attack on full round SPEEDY-7-192 with data complexity of 2 186.2 chosen-plaintexts and time complexity of 2 160.13 encryptions. [ABSTRACT FROM AUTHOR]

Cloud cryptography is the art of converting plain text into an unreadable format, which protects data and prevents the data from being misused by the attacker. Different researchers designed various Caesar cipher algorithms for data security. With the help of these algorithms, the data can be converted into a nonreadable format, but the data cannot be completely secured. In this paper, data security is provided in different phases. Firstly, data are secured through a bit-reversing mechanism in which those replace the actual values with no relation to the original data. Then the four-bit values are added at the beginning and end of bits using a salting mechanism to interlink the salting and existing bit-values and hide the original data. A Caesar cipher value is obtained by applying the Caesar cipher algorithm to the resulting bits. The Caesar cipher algorithm is used to implement number-of-shifting on the obtained values. An efficient cipher matrix algorithm is then developed in which different rules are designed to encrypt the data. Afterward, a secure cipher value is obtained by implementing Cipher XORation rules on the result obtained and the user-defined key. In the end, the proposed algorithm is compared with various papers. It identifies how much better the proposed algorithm performs than all the previous algorithms and how much the attack rate can be reduced if this algorithm is used for data security. [ABSTRACT FROM AUTHOR]

In this paper, we perform neural cryptanalysis on five block ciphers: Data Encryption Standard (DES), Simplified DES (SDES), Advanced Encryption Standard (AES), Simplified AES (SAES), and SPECK. The block ciphers are investigated on three different deep learning-based attacks, Encryption Emulation (EE), Plaintext Recovery (PR), Key Recovery (KR), and Ciphertext Classification (CC) attacks. The attacks attempt to break the block ciphers in various cases, such as different types of plaintexts (i.e., block-sized bit arrays and texts), different numbers of round functions and quantity of training data, different text encryption methods (i.e., Word-based Text Encryption (WTE) and Sentence-based Text Encryption (STE)), and different deep learning model architectures. As a result, the block ciphers can be vulnerable to EE and PR attacks using a large amount of training data, and STE can improve the strength of the block ciphers, unlike WTE, which shows almost the same classification accuracy as the plaintexts, especially in a CC attack. Moreover, especially in the KR attack, the Recurrent Neural Network (RNN)-based deep learning model shows higher average Bit Accuracy Probability than the fully connected-based deep learning model. Furthermore, the RNN-based deep learning model is more suitable than the transformer-based deep learning model in the CC attack. Besides, when the keys are the same as the plaintexts, the KR attack can perfectly break the block ciphers, even if the plaintexts are randomly generated. Additionally, we identify that DES and SPECK32/64 applying two round functions are more vulnerable than those applying the single round function by performing the KR attack with randomly generated keys and randomly generated single plaintext. [ABSTRACT FROM AUTHOR]

Block cipher is a cryptographic field that is now widely applied in various domains. Besides its security, deployment issues, implementation costs, and flexibility across different platforms are also crucial in practice. From an efficiency perspective, the linear layer is often the slowest transformation and requires significant implementation costs in block ciphers. Many current works employ lookup table techniques for linear layers, but they are quite costly and do not save memory storage space for the lookup tables. In this paper, we propose a novel lookup table technique to reduce memory storage when executing software. This technique is applied to the linear layer of block ciphers with recursive Maximum Distance Separable (MDS) matrices, Hadamard MDS matrices, and circulant MDS matrices of considerable sizes (e.g. sizes of 16, 32, 64, and so on). The proposed lookup table technique leverages the recursive property of linear matrices and the similarity in elements of Hadamard or circulant MDS matrices, allowing the construction of a lookup table for a submatrix instead of the entire linear matrix. The proposed lookup table technique enables the execution of the diffusion layer with unchanged computational complexity (number of XOR operations and memory accesses) compared to conventional lookup table implementations but allows a substantial reduction in memory storage for the pre-computed tables, potentially reducing the storage needed by 4 or 8 times or more. The memory storage will be reduced even more as the size of the MDS matrix increases. For instance, analysis shows that when the matrix size is 64, the memory storage ratio with the proposed lookup table technique decreases by 87.5% compared to the conventional lookup table technique. This method also allows for more flexible software implementations of large-sized linear layers across different environments. [ABSTRACT FROM AUTHOR]

This paper delves into the realm of cryptographic analysis by employing mixed-integer linear programming (MILP), a powerful tool for automated cryptanalysis. Building on this foundation, we apply the division property method alongside MILP to conduct a comprehensive cryptanalysis of the IIoTBC (industrial Internet of Things block cipher) algorithm, a critical cipher in the security landscape of industrial IoT systems. Our investigation into IIoTBC System A has led to identifying a 14-round integral distinguisher, further extended to a 22-round key recovery. This significant finding underscores the cipher's susceptibility to sophisticated cryptanalytic attacks and demonstrates the profound impact of combining the division property method with MILP in revealing hidden cipher weaknesses. In the case of IIoTBC System B, our innovative approach has uncovered a full-round distinguisher. We provide theoretical validation for this distinguisher and uncover a pivotal structural issue in the System B algorithm, specifically the non-diffusion of its third branch. This discovery sheds light on inherent security challenges within System B and points to areas for potential enhancement in its design. Our research, through its methodical examination and analysis of the IIoTBC algorithm, contributes substantially to the field of cryptographic security, especially concerning industrial IoT applications. By uncovering and analyzing the vulnerabilities within IIoTBC, we enhance the understanding of cipher robustness and pave the way for advancements in securing industrial IoT communications. [ABSTRACT FROM AUTHOR]

In cryptography, a mode of operation is the way of processing plaintext and encrypted text blocks within a block cipher algorithm, or it is the presentation of a method of chaining blocks in a block cipher. Several models exist with their strengths, some are more vulnerable than others, and some combine authentication and security concepts. In this paper, a new mode of operation is proposed, inspired by the braid of long hair which we call mode CBLCH (Cipher Braided Long Hair Chaining). The HILL cipher will be used to validate it and to compare it with ECB (Electronic Code-Book: code dictionary) and CBC mode (Cipher Block Chaining: sequence of blocks) by the influence of a pixel change on the plaint-image and the encrypted image. [ABSTRACT FROM AUTHOR]

With the rapid evolvement of cryptanalysis, attacks with multiple distinguishers have emerged gradually. Many new cryptanalytic methods such as multiple differential cryptanalysis, multiple linear cryptanalysis, multiple impossible differential cryptanalysis, multidimensional zero correlation linear cryptanalysis have been proposed, which have greatly enhanced the efficiency of corresponding attacks. During these attacks, discovering more distinguishers has always been a trivial and manual work. Many cryptographers use their expertise and experience to achieve this goal. However, in most cases, either the length of the attack or the number of distinguishers is underestimated. This paper proposes a generic method to discover more different distinguishers based on a new property called "weak rotational property". Block ciphers with this property can easily discover more distinguishers such as truncated differential distinguishers, impossible differential distinguishers and zero correlation linear distinguishers in a theoretical approach. Then the number of equivalent distinguishers is proved in a mathematical form. As an application, this paper focuses on SIMON family ciphers to illustrate how this property improves cryptanalysis. For the section of application, first of all, SIMON family ciphers are proved to have weak rotational property. Thus the number of corresponding discovered distinguishers can be increased for SIMON. Then, some earlier observations on SIMON are extended accordingly to this new property. Finally, based on the idea of weak rotational property and equivalent-subkey technique, an improved impossible differential cryptanalysis on SIMON is proposed. For SIMON32(64)/SIMON128(128)/SIMON128(192), the rounds attacked are all extended by one round. For other variants of SIMON, current best non full codebook impossible differential attacks are derived. The successful application of weak rotational property indicates its potential in cryptanalysis. [ABSTRACT FROM AUTHOR]

In symmetric cryptography, block ciphers, stream ciphers and permutations often make use of a round function and many round functions consist of a linear and a non-linear layer. One that is often used is based on the cellular automaton that is denoted by χ as a Boolean map on bi-infinite sequences, F 2 Z . It is defined by σ ↦ ν where each ν i = σ i + (σ i + 1 + 1) σ i + 2 . A map χ n is a map that operates on n-bit arrays with periodic boundary conditions. This corresponds with χ restricted to periodic infinite sequences with period that divides n. This map χ n is used in various permutations, e.g., Keccak-f (the permutation in SHA-3), ASCON (the NIST standard for lightweight cryptography), Xoodoo, Rasta and Subterranean (2.0). In this paper, we characterize the graph of χ on periodic sequences. It turns out that χ is surjective on the set of all periodic sequences. We will show what sequences will give collisions after one application of χ . We prove that, for odd n, the order of χ n (in the group of bijective maps on F 2 n ) is 2 ⌈ lg (n + 1 2) ⌉ . A given periodic sequence lies on a cycle in the graph of χ , or it can be represented as a polynomial. By regarding the divisors of such a polynomial one can see whether it lies in a cycle, or after how many iterations of χ it will. Furthermore, we can see, for a given σ , the length of the cycle in its component in the state diagram. Finally, we extend the surjectivity of χ to F 2 Z , thus to include non-periodic sequences. [ABSTRACT FROM AUTHOR]

Security vulnerabilities in the symmetric-key primitives of a cipher can undermine the overall security claims of the cipher. With the rapid advancement of quantum computing in recent years, there is an increasing effort to evaluate the security of symmetric-key cryptography against potential quantum attacks. This paper focuses on analyzing the quantum attack resistance of AIM, a symmetric-key primitive used in the AIMer digital signature scheme. We present the first quantum circuit implementation of AIM and estimate its complexity (such as qubit count, gate count, and circuit depth) with respect to Grover's search algorithm. For Grover's key search, the most important optimization metric is depth, especially when considering parallel search. Our implementation gathers multiple methods for a low-depth quantum circuit of AIM in order to reduce the Toffoli depth and full depth (such as the Karatsuba multiplication and optimization of inner modules;  Mer ,  LinearLayer). [ABSTRACT FROM AUTHOR]

Meet-in-the-middle (MitM) attack method has led to the best currently published cryptanalytic results on the AES block cipher in the single-key attack scenario, except biclique attack. Particularly, for AES with a 256-bit key (AES-256), Li and Jin published a MitM attack on 10-round AES-256 in 2016, which has a data complexity of 2 111 chosen plaintexts, a memory complexity of 2 215.2 bytes and a time complexity of 2 253 10-round AES-256 encryptions under so-called weak-key approach. In this paper, we observe that the memory complexity of Li and Jin's attack should be 2 217.4 bytes, then we show that three other byte key relations can be used to further reduce the memory complexity in Li and Jin's attack by decomposing Li and Jin's big precomputational table into two smaller ones and using MixColumns' property to connect the two smaller tables in online key-recovery phase, which produces a 10-round AES-256 attack with a memory complexity of 2 189 bytes and a time complexity of 2 255 10-round AES encryptions, and finally we exploit a different 6-round MitM distinguisher to mount a 10-round AES-256 attack with a data complexity of 2 105 chosen plaintexts, a memory complexity of 2 189 bytes and a time complexity of 2 253.2 10-round AES encryptions. Our final attack has a much smaller data and memory complexity and a marginally larger time complexity than Li and Jin's attack. [ABSTRACT FROM AUTHOR]

In modern information technology, research on block cipher security is imperative. Concerning the ultra lightweight block cipher PICO, there has been only one study focused on recovering its complete master key, with a large search space of 2 64 , and no fault analysis yet. This paper proposes a new fault analysis approach, combining differential fault and algebraic equation techniques. It achieved the recovery of PICO's entire master key with 40 faults in an average time of 0.57 h. S-box decomposition was utilized to optimize our approach, reducing the time by a remarkable 75.83% under the identical 40-fault condition. Furthermore, PICO's complete master key could be recovered with 28 faults in an average time of 0.78 h, indicating a significant 2 37 reduction in its search space compared to the previous study. This marks the first fault analysis on PICO. Compared to conventional fault analysis methods DFA (differential fault analysis) and AFA (algebraic fault analysis), our approach outperforms in recovering PICO's entire master key, highlighting the cruciality of key expansion complexity in block cipher security. Therefore, our approach could serve to recover master keys of block ciphers with comparably complicated key expansions, and production of more secure block ciphers could result. [ABSTRACT FROM AUTHOR]

The SAND algorithm is a family of lightweight AND-RX block ciphers released by DCC in 2022. Our research focuses on assessing the security of SAND with a quantum computation model. This paper presents the first quantum implementation of SAND (including two versions of SAND, SAND-64 and SAND-128). Considering the depth-times-width metric, the quantum circuit implementation of the SAND algorithm demonstrates a relatively lower consumption of quantum resources than that of the quantum implementations of existing lightweight algorithms. A generalized Grover-based brute-force attack framework was implemented and employed to perform attacks on two versions of the SAND algorithm. This framework utilized the g-database algorithm, which considered different plaintext–ciphertext pairs in a unified manner, reducing quantum resource consumption. Our findings indicate that the SAND-128 algorithm achieved the NIST security level I, while the SAND-64 algorithm fell short of meeting the requirements of security level I. [ABSTRACT FROM AUTHOR]

This paper presents a study aimed at effectively implementing a deterministic random bit generator (DRBG) IP in verilog language, based on the standard encryption algorithm. By controlling the existing round generation and key generation blocks, the internal modules of the counter mode deterministic random bit generator (CTR-DRBG) were successfully implemented and operated, ensuring the secure and efficient generation of random bit sequences. The research focused on parallel operation of modules and optimized module placement to achieve improved clock frequencies. By concurrently operating two modules in the derivation and internal update modules of CTR-DRBG, the processing speed was enhanced compared to the conventional algorithm. Additionally, integrating the reseeding and initialization modules of CTR-DRBG into a single module successfully reduced size. Furthermore, this IP supports the special function register (SFR) interface. The safety of the CTR-DRBG was validated through known answer test (KAT) verification utilizing test vectors from certification. Future research should explore additional studies on CTR-DRBG operating on real FPGA or ASIC, not only using normal algorithm but also employing other block cipher algorithms. [ABSTRACT FROM AUTHOR]

Cryptographic devices' complexity necessitates fast security simulation environments against fault attacks. SystemC, a promising candidate in Electronic System Levels (ESLs), can achieve higher simulation speeds while maintaining accuracy and reliability, and its modular and hierarchical design allows for efficient modeling of complex cryptographic algorithms and protocols. However, code modification is required for fault injection and detection. Aspect-Oriented Programming (AOP) can test cryptographic models' robustness without modifications, potentially replacing real cryptanalysis schemes and reducing the time and effort required for fault injection and detection. Through the utilization of a fault injection/detection environment, this paper presents a novel approach to simulating the security fault attacks of ASCON cryptographic systems at the ESL. The purpose of this methodology is to evaluate the resistance of ASCON SystemC models against fault attacks. The proposed methodology leverages the advantages of AOP to enhance the fault injection and detection process. By applying AOP techniques, we inject faults into the SystemC models without making any changes to the main codebase. This approach not only improves the efficiency of testing cryptographic systems but also ensures that the main functionality remains intact during the fault injection process. The methodology was validated using three scenarios and SystemC ASCON as a case study. The first simulation involved evaluating fault detection capabilities, the second focused on the impact of AOP on executable file size and simulation time, and the third focused on the ESL impact on the ASCON design process. Simulation results show that this methodology can perfectly evaluate the robustness of the ASCON design against fault injection attacks with no significant impact on simulation time and file executable size. Additionally, the simulation results prove that the ASCON development life cycle at the ESL reduces the amount of time devoted to the design procedure by 83.34%, and the ASCON security attack simulations at the ESL decrease the simulation time by 40% compared to the register transfer level (RTL). [ABSTRACT FROM AUTHOR]

To obtain reliable results from conflicting data in mobile crowdsensing, numerous truth discovery protocols have been proposed in the past decade. However, most of them do not consider the data privacy of entities involved (e.g., workers and servers), and several existing privacy-preserving truth discovery protocols either provide limited privacy protection or have heavy computation and communication overheads due to iterative computation and transmission over large ciphertexts. In this paper, we aim to propose privacy-preserving and lightweight truth discovery protocols to tackle the above problems. Specifically, we carefully design an anonymization protocol named AnonymTD to delink workers from their data, where workers’ data are computed and transmitted without complicated encryption. To further reduce each worker's overheads in the scenarios where workers are willing to share their weights, we resort to the perturbation technology to propose a more lightweight truth discovery protocol named PerturbTD. Based on workers’ perturbed data, two cloud servers in PerturbTD complete most of the workload of truth discovery together, which avoids the frequent involvement of workers. The theoretical analysis and the comparative experiments in this paper demonstrate that our two protocols can achieve our security goals with low computation and communication overheads. [ABSTRACT FROM AUTHOR]

The latest version of ZigBee offers improvements in various aspects, including its low power consumption, flexibility, and cost-effective deployment. However, the challenges persist, as the upgraded protocol continues to suffer from a wide range of security weaknesses. Constrained wireless sensor network devices cannot use standard security protocols such as asymmetric cryptography mechanisms, which are resource-intensive and unsuitable for wireless sensor networks. ZigBee uses the Advanced Encryption Standard (AES), which is the best recommended symmetric key block cipher for securing data of sensitive networks and applications. However, AES is expected to be vulnerable to some attacks in the near future. Moreover, symmetric cryptosystems have key management and authentication issues. To address these concerns in wireless sensor networks, particularly in ZigBee communications, in this paper, we propose a mutual authentication scheme that can dynamically update the secret key value of device-to-trust center (D2TC) and device-to-device (D2D) communications. In addition, the suggested solution improves the cryptographic strength of ZigBee communications by improving the encryption process of a regular AES without the need for asymmetric cryptography. To achieve that, we use a secure one-way hash function operation when D2TC and D2D mutually authenticate each other, along with bitwise exclusive OR operations to enhance cryptography. Once authentication is accomplished, the ZigBee-based participants can mutually agree upon a shared session key and exchange a secure value. This secure value is then integrated with the sensed data from the devices and utilized as input for regular AES encryption. By adopting this technique, the encrypted data gains robust protection against potential cryptanalysis attacks. Finally, a comparative analysis is conducted to illustrate how the proposed scheme effectively maintains efficiency in comparison to eight competitive schemes. This analysis evaluates the scheme's performance across various factors, including security features, communication, and computational cost. [ABSTRACT FROM AUTHOR]

As IoT technology develops, many sensor devices are being used in our life. To protect such sensor data, lightweight block cipher techniques such as SPECK-32 are applied. However, attack techniques for these lightweight ciphers are also being studied. Block ciphers have differential characteristics, which are probabilistically predictable, so deep learning has been utilized to solve this problem. Since Gohr's work at Crypto2019, many studies on deep-learning-based distinguishers have been conducted. Currently, as quantum computers are developed, quantum neural network technology is developing. Quantum neural networks can also learn and make predictions on data, just like classical neural networks. However, current quantum computers are constrained by many factors (e.g., the scale and execution time of available quantum computers), making it difficult for quantum neural networks to outperform classical neural networks. Quantum computers have higher performance and computational speed than classical computers, but this cannot be achieved in the current quantum computing environment. Nevertheless, it is very important to find areas where quantum neural networks work for technology development in the future. In this paper, we propose the first quantum neural network based distinguisher for the block cipher SPECK-32 in an NISQ. Our quantum neural distinguisher successfully operated for up to 5 rounds even under constrained conditions. As a result of our experiment, the classical neural distinguisher achieved an accuracy of 0.93, but our quantum neural distinguisher achieved an accuracy of 0.53 due to limitations in data, time, and parameters. Due to the constrained environment, it cannot exceed the performance of classical neural networks, but it can operate as a distinguisher because it has obtained an accuracy of 0.51 or higher. In addition, we performed an in-depth analysis of the quantum neural network's various factors that affect the performance of the quantum neural distinguisher. As a result, it was confirmed that the embedding method, the number of the qubit, and quantum layers, etc., have an effect. It turns out that if a high-capacity network is needed, we have to properly tune properly to take into account the connectivity and complexity of the circuit, not just by adding quantum resources. In the future, if more quantum resources, data, and time become available, it is expected that an approach to achieve better performance can be designed by considering the various factors presented in this paper. [ABSTRACT FROM AUTHOR]

Chaos has been one of the most effective cryptographic sources since it was first used in image-encryption algorithms. This paper closely examines the development process of chaos-based image-encryption algorithms from various angles, including symmetric and asymmetric algorithms, block ciphers and stream ciphers, and integration with other technologies. The unique attributes of chaos, such as sensitivity to initial conditions, topological transitivity, and pseudo-randomness, are conducive to cross-referencing with other disciplines and improving image-encryption methods. Additionally, this paper covers practical application scenarios and current challenges of chaotic image encryption, thereby encouraging researchers to continue developing and complementing existing situations, and may also serve as a basis of future development prospects for chaos-based image encryption. [ABSTRACT FROM AUTHOR]

Lightweight block ciphers are normally used in low-power resource-constrained environments, while providing reliable and sufficient security. Therefore, it is important to study the security and reliability of lightweight block ciphers. SKINNY is a new lightweight tweakable block cipher. In this paper, we present an efficient attack scheme for SKINNY-64 based on algebraic fault analysis. The optimal fault injection location is given by analyzing the diffusion of a single-bit fault at different locations during the encryption process. At the same time, by combining the algebraic fault analysis method based on S-box decomposition, the master key can be recovered in an average time of 9 s using one fault. To the best of our knowledge, our proposed attack scheme requires fewer faults, is faster to solve, and has a higher success rate than other existing attack methods. [ABSTRACT FROM AUTHOR]

Neural cryptography is a technique that uses neural networks for secure data encryption. Cryptoanalysis, on the other hand, deals with analyzing and decrypting ciphers, codes, and encrypted text without using a real key. Chosen-plaintext cryptanalysis is a subfield of cryptanalysis where both plain text and ciphertext are available and the goal is either to find the encryption technique, the encryption key, or both. This study addresses chosen plaintext cryptanalysis within public key cryptography, to categorize topics of encrypted text. Using a fixed encryption technique and key, the focus was placed on creating a framework that identifies the topic associated with ciphertext, using diverse plaintexts and their corresponding cipher texts. To our knowledge, this is the first time that chosen-plaintext cryptanalysis has been discussed in the context of topic modeling. The paper used deep learning techniques such as CNNs, GRUs, and LSTMs to process sequential data. The proposed framework achieved up to 67% precision, 99% recall, 80% F1-score, and 71% AUPR on a dataset, showcasing promising results and opening avenues for further research in this cryptanalysis subarea. [ABSTRACT FROM AUTHOR]

The point-to-point key distribution technology based on twinning semiconductor superlattice devices can provide high-speed secure symmetric keys, suitable for scenarios with high security requirements such as the one-time pad cipher. However, deploying these devices and scaling them in complex scenarios, such as many-to-many communication, poses challenges. To address this, an effective solution is to build a secure key distribution network for communication by selecting trusted relays and deploying such devices between them. The larger the network, the higher the likelihood of relay node failure or attack, which can impact key distribution efficiency and potentially result in communication key leakage. To deal with the above challenges, this paper proposes an on-demand fault-tolerant routing strategy based on the secure key distribution network to improve the fault tolerance of the network while ensuring scalability and availability. The strategy selects the path with better local key status through a fault-free on-demand path discovery mechanism. To improve the reliability of the communication key, we integrate an acknowledgment-based fault detection mechanism in the communication key distribution process to locate the fault, and then identified the cause of the fault based on the Dempster–Shafer evidence theory. The identified fault is then isolated through subsequent path discovery and the key status is transferred. Simulation results demonstrate that the proposed method outperforms OSPF, the adaptive stochastic routing and the multi-path communication scheme, achieving an average 20 % higher packet delivery ratio and lower corrupted key ratio, thus highlighting its reliability. Additionally, the proposed solution exhibits a relatively low local key overhead, indicating its practical value. [ABSTRACT FROM AUTHOR]

In this paper, we study the security of the Key-Alternating Feistel (KAF) ciphers, a class of key alternating ciphers with the Feistel structure, where each round of the cipher is instantiated with n-bit public round permutation P i , namely the i-th round of the cipher maps (X L , X R) ↦ (X R , P i (X R ⊕ K i) ⊕ K i ⊕ X L). We have shown that our 5 round construction with independent round permutations and independent round keys achieves 2n/3-bit security in the random permutation model, i.e., the setting where the adversary is allowed to make forward and inverse queries to the round permutations in a black box way. [ABSTRACT FROM AUTHOR]

Mixed-integer linear programming (MILP) techniques are widely used in cryptanalysis, aiding in the discovery of optimal linear and differential characteristics. This paper delves into the analysis of block ciphers KLEIN and PRINCE using MILP, specifically calculating the best linear and differential characteristics for reduced-round versions. Both ciphers employ matrix multiplication in their diffusion layers, which we model using multiple XOR operations. To this end, we propose two novel MILP models for multiple XOR operations, which use fewer variables and constraints, proving to be more efficient than standard methods for XOR modeling. For differential cryptanalysis, we identify characteristics with a probability of 2-59 for 7 rounds of KLEIN and a probability of 2-56 for 7 rounds of PRINCE. In linear cryptanalysis, we identify characteristics with a bias of 2-27 for 6 rounds of KLEIN and a bias of 2-29 for 7 rounds of PRINCE. These results establish the best single-key differential and linear distinguishers for these ciphers in the literature. [ABSTRACT FROM AUTHOR]

Lightweight block ciphers are the essential encryption algorithm for devices with limited resources. Its goal is to ensure the security of data transmission through resource-constrained devices. Impossible differential cryptanalysis is one of the most effective cryptanalysis on block ciphers, and assessing the ability of resisting this attack is a basic design criterion. Shadow is a lightweight block cipher proposed by Guo et al. (IEEE Internet Things J 8(16):13014–13023, 2021). It utilizes a combination of ARX operations and generalized Feistel structure to overcome the weakness of the traditional Feistel structure that only diffuses half in one round. In this paper, we focus on the differential property of Shadow and its security against impossible differential cryptanalysis. First, we use the SAT method to automatically search for a full-round impossible differential distinguisher of Shadow-32. Then, based on the experimental results, we prove that Shadow has a differential property with probability 1 based on the propagation of the state. Further, we can obtain an impossible differential distinguisher for an arbitrary number of rounds of Shadow. Finally, we perform a full key recovery attack on the full-round Shadow-32 and Shadow-64. Both experimentally and theoretically, our results indicate that Shadow is critically flawed, and regardless of the security strength of the internal components and the number of rounds applied, the overall cipher remains vulnerable to impossible differential cryptanalysis. [ABSTRACT FROM AUTHOR]

Attacks on the Internet of Things (IoT) are not highly considered during the design and implementation. The prioritization is making profits and supplying services to clients. Most cryptographic algorithms that are commonly used on the IoT are vulnerable to attacks such as linear, differential, differential–linear cryptanalysis attacks, and many more. In this study, we focus only on linear cryptanalysis attacks. Little has been achieved (by other researchers) to prevent or block linear cryptanalysis attacks on cryptographic algorithms used on the IoT. In this study, we managed to block the linear cryptanalysis attack using a mathematically novel approach called Galois Field of the order (232), denoted by GF (232), and high irreducible polynomials were used to re-construct weak substitution boxes (S-Box) of mostly cryptographic algorithms used on IoT. It is a novel approach because no one has ever used GF (232) and highly irreducible polynomials to block linear cryptanalysis attacks on the most commonly used cryptographic algorithms. The most commonly used cryptographic algorithms on the IoT are Advanced Encryption Standard (AES), BLOWFISH, CAMELLIA, CAST, CLEFIA, Data Encryption Standard (DES), Modular Multiplication-based Block (MMB), RC5, SERPENT, and SKIPJACK. We assume that the reader of this paper has basic knowledge of the above algorithms. [ABSTRACT FROM AUTHOR]

In the context of linear cryptanalysis of block ciphers, let p 0 (resp. p 1 ) be the probability that a particular linear approximation holds for the right (resp. a wrong) key choice. The standard right key randomisation hypothesis states that p 0 is a constant p ≠ 1 / 2 and the standard wrong key randomisation hypothesis states that p 1 = 1 / 2 . Using these hypotheses, the success probability P S of the attack can be expressed in terms of the data complexity N. The resulting expression for P S is a monotone increasing function of N. Building on earlier work by O'Connor (In: Preneel B (ed) Fast Software Encryption: Second International Workshop. Leuven, Belgium, 14–16 December 1994, Proceedings, volume 1008 of Lecture Notes in Computer Science, pp. 131–136. Springer, 1994) and Daemen and Rijmen (J Math Cryptol 1(3):221–242, 2007), Bogdanov and Tischhauser (In: Moriai S (ed) Fast Software Encryption—20th International Workshop, FSE 2013, Singapore, March 11–13, 2013. Revised Selected Papers, volume 8424 of Lecture Notes in Computer Science, pp. 19–38. Springer, 2013) argued that p 1 should be considered to be a random variable. They postulated the adjusted wrong key randomisation hypothesis which states that p 1 follows a normal distribution. A non-intuitive consequence is that the resulting expression for P S is no longer a monotone increasing function of N. A later work by Blondeau and Nyberg (Des Codes Cryptogr 82(1–2):319–349, 2017) argued that p 0 should also be considered to be a random variable and they postulated the adjusted right key randomisation hypothesis which states that p 0 follows a normal distribution. In this work, we revisit the key randomisation hypotheses. While the argument that p 0 and p 1 should be considered to be random variables is indeed valid, we show that if p 0 and p 1 follow any distributions with supports which are subsets of [0, 1], and E [ p 0 ] = p and E [ p 1 ] = 1 / 2 , then the expression for P S that is obtained is exactly the same as the one obtained using the standard key randomisation hypotheses. Consequently, P S is a monotone increasing function of N even when p 0 and p 1 are considered to be random variables. [ABSTRACT FROM AUTHOR]

Physical unclonable functions (PUF) are cryptographic primitives employed to generate true and intrinsic randomness which is critical for cryptographic and secure applications. Thus, the PUF output (response) has properties that can be utilized in building a true random number generator (TRNG) for security applications. The most popular PUF architectures are transistor-based and they focus on exploiting the uncontrollable process variations in conventional CMOS fabrication technology. Recent development in emerging technology such as memristor-based models provides an opportunity to achieve a robust and lightweight PUF architecture. Memristor-based PUF has proven to be more resilient to attacks such as hardware reverse engineering attacks. In this paper, we design a lightweight and low-cost memristor PUF and verify it against cryptographic randomness tests achieving a unique, reliable, irreversible random sequence output. The current research demonstrates the architecture of a low-cost, high endurance Cu/HfO 2 / p + + Si memristor-based PUF (MR-PUF) which is compatible with advanced CMOS technologies. This paper explores the 15 NIST cryptographic randomness tests that have been applied to our Cu/HfO 2 / p + + Si MR-PUF. Moreover, security properties such as uniformity, uniqueness, and repeatability of our MR-PUF have been tested in this paper and validated. Additionally, this paper explores the applicability of our MR-PUF on block ciphers to improve the randomness achieved within the encryption process. Our MR-PUF has been used on block ciphers to construct a TRNG cipher block that successfully passed the NIST tests. Additionally, this paper investigated MR-PUF within a new authenticated key exchange and mutual authentication protocol between the head-end system (HES) and smart meters (SM)s in an advanced metering infrastructure (AMI) for smartgrids. The authenticated key exchange protocol utilized within the AMI was verified in this paper to meet the essential security when it comes to randomness by successfully passing the NIST tests without a post-processing algorithm. [ABSTRACT FROM AUTHOR]

SIMON is one of the lightweight block ciphers designed by the National Security Agency in 2013, and a technical report including security analysis was published by the design team nearly 4 years later. As for the linear attack, it is claimed that 'the single‐path probabilities (and linear correlations) dip below 2−block size for 12, 16, 20, 29, and 38 rounds for SIMON32, 48, 64, 96, and 128, respectively'. However, the design team does not show details on how to get the result and there are also no published papers verified the result yet. In the present paper, an upper bound of squared correlation of SIMON‐like functions is given. As an important application of this bound, how to find optimal linear characteristics of SIMON and SIMECK under the Markov assumption with Matsui's branch‐and‐bound algorithm is shown. The authors' results confirm the claim of the design team. Furthermore, the best‐known linear‐hull distinguishers for SIMON and SIMECK is also given. [ABSTRACT FROM AUTHOR]

Multivariate public‐key cryptography (MPKC) is considered a leading candidate for post‐quantum cryptography (PQC). It is based on the hardness of the multivariate quadratic polynomial (MQ) problem, which is a problem of finding a solution to a system of quadratic equations over a finite field. In this paper, we survey some recent progress in the security analysis of MPKC. Among various existing multivariate schemes, the most important one is the Rainbow signature scheme proposed by Ding et al. in 2005, which was later selected as a finalist in the third round of the PQC standardization project by the National Institute of Standards and Technology. Under the circumstances, some recent research studies in MPKC have focussed on the security analysis of the Rainbow scheme. In this paper, the authors first explain efficient algorithms for solving the MQ problem and the research methodology for estimating their complexity in MPKC. Then, the authors survey some recent results related to the security analysis of the Rainbow scheme. In particular, the authors provide a detailed description of the complexity analysis for solving the bi‐graded polynomial systems studied independently by Nakamura et al. and Smith‐Tone et al., and then expound the rectangular MinRank attack against Rainbow proposed by Beullens. [ABSTRACT FROM AUTHOR]

Internet of Things devices, platform programs, and network applications are all vulnerable to cyberattacks (digital attacks), which can be prevented at different levels by using cybersecurity protocol. In the Internet of Things (IoT), cyberattacks are specifically intended to retrieve or change/destroy sensitive information that may exceed the IoT's advantages. Furthermore, the design of a lightweight cybersecurity mechanism experiences a critical challenge that would perfectly fit resource-constrained IoT devices. For instance, identifying the compromised devices and the users' data and services protection are the general challenges of cybersecurity on an IoT system that should be considered. This paper proposes a secure cybersecurity system based on the integration of cryptography with authentication (ELCA) that utilizes elliptic curve Diffie–Hellman (ECDH) to undertake key distribution while the weak bits problem in the shared secret key is resolved. In this paper, three systems of integration are investigated, while ELCA proposes secure integration between authentication and encryption to facilitate confidentiality and authenticity transfer messages between IoT devices over an insecure communication channel. Furthermore, the security of ELCA is proven mathematically using the random oracle model and IoT adversary model. The findings of the emulation results show the effectiveness of ELCA performance in terms of a reduced CPU execution time by 50%, reduced storage cost by 32–19.6%, and reduced energy consumption by 41% compared to the baseline cryptographic algorithms. [ABSTRACT FROM AUTHOR]

In terms of security, MDS matrices are one of the best choices for the diffusion layer of block ciphers. However, as these matrices grow in size, their software implementation becomes a challenge. In this paper, to benefit from the properties of MDS matrices and avoid the mentioned challenge, we use 4 × 4 MDS matrices to build some 16 × 16 matrices with a low number of zero elements. We show that if these matrices are used as diffusion layers of software-based SPN structures, the resulting block ciphers have similar properties as AES in software implementation complexity (i.e. the number of required CPU instructions) and resistance against linear and differential attacks. Moreover, the best impossible differential and square distinguishers for the proposed 16 × 16 structures have a similar length as SPN structures with 16 × 16 MDS matrices. Thus, the new structures outperform AES concerning the impossible differential and square attacks. Additionally, we show that if the proposed SPN structure uses the AES key schedule, its results for the differential related-key attacks are better than those for AES. We also extend the idea and use 4×4 MDS matrices to design 24×24 and 32×32 matrices with acceptable properties for SPN structure design. Finally, we extend the idea to propose some matrices for Feistel structures with SP-type F-functions. We show that the resulting structures are more secure than the improved Type-II GFS. [ABSTRACT FROM AUTHOR]

Evaluating the security of a block cipher against impossible differential cryptanalysis, is an important aspect during the design process. The maximum length of impossible differentials is often used to evaluate this security. There have been many methods on giving upper bounds on the length of impossible differentials or finding longer impossible differentials. Two notable examples are the "Primitive Index" method proposed by Sun et al. at EUROCRYPT2016 and the MILP method proposed by Sasaki et al. at EUROCRYPT2017. However, these existing methods can only give upper bounds for some special SPN block ciphers or cannot give upper bounds due to the high time complexity. In this paper, we show that when ignoring the differential property of the underlying S-box, giving upper bounds on the length of impossible differentials is a linear problem. By using linear algebra, we propose the Expansion Index of the linear layer, with which we can give upper bounds on the length of impossible differentials for any SPN block cipher with the detail of the S-box omitted. The core of this method is establishing and solving systems of linear equations, thus the verification of a single differential has linear time complexity. What's more, to give upper bounds with this method, we only need to establish and solve systems for differentials whose input and output differences have only one active S-box, which greatly reduces its time complexity from O (2 t) to O(t) (here t denotes the number of S-boxes in the S-layer). The method in this paper is implemented in C and encapsulated into a tool freely available to readers. By applying our method on some SPN block ciphers, we give, for the first time, upper bounds on the length of impossible differentials for Midori, Skinny, CRYPTON, mCrypton, Minalpher. [ABSTRACT FROM AUTHOR]

In CRYPTO 2019, Gohr opens up a new direction for cryptanalysis. He successfully applied deep learning to differential cryptanalysis against the NSA block cipher SPECK32/64, achieving higher accuracy than traditional differential distinguishers. Until now, one of the mainstream research directions is increasing the training sample size and utilizing different neural networks to improve the accuracy of neural distinguishers. This conversion mindset may lead to a huge number of parameters, heavy computing load, and a large number of memory in the distinguishers training process. However, in the practical application of cryptanalysis, the applicability of the attacks method in a resource-constrained environment is very important. Therefore, we focus on the cost optimization and aim to reduce network parameters for differential neural cryptanalysis.In this paper, we propose two cost-optimized neural distinguisher improvement methods from the aspect of data format and network structure, respectively. Firstly, we obtain a partial output difference neural distinguisher using only 4-bits training data format which is constructed with a new advantage bits search algorithm based on two key improvement conditions. In addition, we perform an interpretability analysis of the new neural distinguishers whose results are mainly reflected in the relationship between the neural distinguishers, truncated differential, and advantage bits. Secondly, we replace the traditional convolution with the depthwise separable convolution to reduce the training cost without affecting the accuracy as much as possible. Overall, the number of training parameters can be reduced by less than 50% by using our new network structure for training neural distinguishers. Finally, we apply the network structure to the partial output difference neural distinguishers. The combinatorial approach have led to a further reduction in the number of parameters (approximately 30% of Gohr's distinguishers for SPECK). [ABSTRACT FROM AUTHOR]

As the first generic method for finding the optimal differentialand linear characteristics, Matsui's branch and bound search algorithm has played an important role in evaluating the security of symmetric ciphers. By combining Matsui's bounding conditions with automatic search models, search efficiency can be improved. In this paper, by studying the properties of Matsui's bounding conditions, we give the general form of bounding conditions that can eliminate all the impossible solutions determined by Matsui's bounding conditions. Then, a new method of combining bounding conditions with sequential encoding method is proposed. With the help of some small size Mixed Integer Linear Programming (MILP) models, we can use fewer variables and clauses to build Satisfiability Problem (SAT) models. As applications, we use our new method to search for the optimal differential and linear characteristics of some SPN, Feistel, and ARX block ciphers. The number of variables and clauses and the solving time of the SAT models are decreased significantly. In addition, we find some new differential and linear characteristics covering more rounds. [ABSTRACT FROM AUTHOR]

In this paper, we propose a design to detect and prevent IP spoofing-based distributed denial of service (DDoS) attacks on software-defined networks (SDNs). DDoS attacks are still one of the significant problems for internet service providers (ISPs) and individual users. These attacks can disrupt customer services by targeting the availability of the system, and in some cases, they can completely shut down the target infrastructure. Protecting the system against DDoS attacks is therefore crucial for ensuring the reliability and availability of internet services. To address this problem, we propose a lightweight source address validation (LSAV) framework that leverages the flexibility of SDN architecture in ISP networks and employs a lightweight filtering mechanism that considers the cost of operation to maintain high performance. Our setup for the proposed mechanism reflects client–server communication through an ISP SDN, and we use the entry points to eliminate malicious user requests targeting the systems. We then propose a novel algorithm on top of this setup to introduce a new and more efficient approach to existing mitigation methodologies. In addition to filtering the traffic against IP spoofing-based DDoS attacks, LSAV also prioritizes low resource consumption and high performance in terms of delay and bandwidth. With this approach, we believe that ISPs can effectively defend against IP spoofing-based DDoS attacks while still preserving low resource consumption for the infrastructure and high-quality internet services for their customers. [ABSTRACT FROM AUTHOR]

It is challenging to devise lightweight cryptographic primitives efficient in both hardware and software that can provide an optimum level of security to diverse Internet of Things applications running on low-end constrained devices. Therefore, an efficient hardware design approach that requires some specific hardware resource may not be efficient if implemented in software. Substitution bit Permutation Network based ciphers such as PRESENT and GIFT are efficient, lightweight cryptographic hardware design approaches. These ciphers introduce confusion and diffusion by employing a 4 × 4 static substitution box and bit permutations. The bit-wise permutation is realised by simple rerouting, which is most cost-effective to implement in hardware, resulting in negligible power consumption. However, this method is highly resource-consuming in software, particularly for large block-sized ciphers, with each single-bit permutation requiring multiple sub-operations. This paper proposes a novel software-based design approach for permutation operation in Substitution bit Permutation Network based ciphers using a bit-banding feature. The conventional permutation using bit rotation and the proposed approach have been implemented, analysed and compared for GIFT and PRESENT ciphers on ARM Cortex-M3-based LPC1768 development platform with KEIL MDK used as an Integrated Development Environment. The real-time performance comparison between conventional and the proposed approaches in terms of memory (RAM/ROM) footprint, power, energy and execution time has been carried out using ULINKpro and ULINKplus debug adapters for various code and speed optimisation scenarios. The proposed approach substantially reduces execution time, energy and power consumption for both PRESENT and GIFT ciphers, thus demonstrating the efficiency of the proposed method for Substitution bit Permutation Network based symmetric block ciphers. [ABSTRACT FROM AUTHOR]

When it comes to data storage, cloud computing and cloud storage providers play a critical role. The cloud data can be accessed from any location with an internet connection. Additionally, the risk of losing privacy when data is stored in a cloud environment is also increased. A variety of security techniques are employed in the cloud to enhance security. In this paper, we aim at maintaining the privacy of stored data in cloud environment by implementing block-based modelling to boost the privacy level with Anti-Codify Technique (ACoT) and block cipher-based algorithms. Initially, the cipher text is generated using Deoxyribo Nucleic Acid (DNA) model. Block-cipher-based encryption is used by ACoT, but the original encrypted file and its extension are broken up into separate blocks. When the original file is broken up into two separate blocks, it raises the security level and makes it more difficult for outsiders to cloud data access. ACoT improves the security and privacy of cloud storage data. Finally, the fuzzy-based classification is used that stores various access types in servers. The simulation results shows that the ACoT-DNA method achieves higher entropy against various block size with reduced computational cost than existing methods. [ABSTRACT FROM AUTHOR]

Recent advances in mobile connection technology have been involved in every aspect of modern life. Even vehicles are becoming more connected, with the ability to communicate without human intervention. The main idea of connected vehicles is to exchange information to avoid a potential collision or to warn the driver about stop signs/lights. To achieve a wide range of connections between vehicles, they must be equipped with connected devices such as Bluetooth, wi-fi, and cellular connectivity. However, communication raises security issues with regard to cybersecurity attacks that attempt to collect confidential information or to take control of the vehicle by forcing unintended braking or steering. In this paper, we proposed a secure vehicle-to-vehicle (V2V) communication approach by deploying a secure communication protocol based on a key management process and a cryptography system to encrypt exchanged data. The proposed key management process was designed to resist many attacks and eliminate connections to the infrastructure for key generation. Since vehicles are equipped with embedded devices with limited computation resources, a lightweight cryptography algorithm was used. The light encryption device (LED) block cipher was used to encrypt exchanged data. The LED has a low implementation area on hardware and low power consumption. It is considered to be a perfect solution for security issues in connected vehicles. The proposed data encryption algorithm was synthesized with VHDL on the Xilinx Zynq-7020 FPGA using the Vivado HLS tool. The encryption algorithm was implemented only on the logic of the device. The achieved results proved that the proposed algorithm is suitable for implementation in vehicles due to its low implementation requirements and low power consumption in addition to its high security level against cyber-attacks. [ABSTRACT FROM AUTHOR]

Cryptosystem cryptanalysis is regarded as an NP-Hard task in modern cryptography. Due to block ciphers that are part of a modern cipher and have nonlinearity and low autocorrelation in their structure, traditional techniques and brute-force attacks suffer from breaking the key presented in traditional techniques, and brute-force attacks against modern cipher S-AES (simplified-advanced encryption standard) are complex. Thus, developing robust and reliable optimization with high searching capability is essential. Motivated by this, this paper attempts to present a novel binary hybridization algorithm based on the mathematical procedures of the grey wolf optimizer (GWO) and particle swarm optimization (PSO), named BPSOGWO, to deal with the cryptanalysis of (S-AES). The proposed BPSOGWO employs a known plaintext attack that requires only one pair of plaintext–ciphertext pairs instead of other strategies that require more pairs (i.e., it reduces the number of messages needed in an attack, and secret information such as plaintext-ciphertext pairs cannot be obtained easily). The comprehensive and statistical results indicate that the BPSOGWO is more accurate and provides superior results compared to other peers, where it improved the cryptanalysis accurateness of S-AES by 82.5%, 84.79%, and 79.6% compared to PSO, GA, and ACO, respectively. Furthermore, the proposed BPSOGWO retrieves the optimal key with a significant reduction in search space compared to a brute-force attack. Experiments show that combining the suggested fitness function with HPSOGWO resulted in a 109-fold reduction in the search space. In cryptanalysis, this is a significant factor. The results prove that BPSOGWO is a promising and effective alternative to attack the key employed in the S-AES cipher. [ABSTRACT FROM AUTHOR]

This research paper presents a study on designing and implementing a robust key management scheme for lightweight block ciphers in Internet of Things (IoT) networks. Key management is a critical concern for IoT devices due to their limited resources and susceptibility to security threats. The proposed scheme utilises partial key pre-distribution to achieve lightweight and secure key management. The protocol's security has been analysed against various attacks, demonstrating its resistance. Performance evaluation results indicate that the proposed key management technique is suitable for resource-constraint IoT networks, as it reduces communication overhead, power consumption, and storage space requirements. The methodology employed in this research includes designing and implementing the proposed key management scheme and conducting scenario-based analyses of its functionality. The results affirm that the proposed solution effectively ensures secure communication in IoT networks. Overall, this research contributes to developing a secure and efficient key management scheme for lightweight block ciphers in IoT networks. [ABSTRACT FROM AUTHOR]

This paper investigates three substitution box (S-box) designs for lightweight ciphers. The proposed designs involve functional decomposition, which is preferable as compared to the look-up tables or logic gates-based S-box designs due to their advantages, as outlined in this paper. The aim is to reduce the number of literals at the input of the S-box to achieve resource reduction in the overall circuit design of the encryption algorithms. The proposed S-box architectures provide optimized resource mapping for specific target hardware, and high throughput along with lesser energy consumption compared to state-of-the-art designs. The proposed S-box designs are applied to the substitution layers of symmetric lightweight block ciphers with a standard security level. In order to be fair in comparison, the overall cipher designs are targeted for similar FPGA-based implementation as used in existing works, while the architectures of the individual 4-bit S-boxes are evaluated using SAED90 nm standard cell libraries. The performance of the proposed S-box designs is observed to be superior at RFID and ISM frequency ranges which can be deployed in applications constrained to low resources as well as demanding high performance and medium security. • Three S-box architectures are proposed using functional decomposition techniques. • The proposed designs are applied to the substitution layer of each cipher i.e., GIFT, Midori, PRESENT and RECTANGLE. • The throughput and energy of the architectures are evaluated for RFID and ISM band frequencies to manifest the utility of lightweight ciphers in IoT applications. [ABSTRACT FROM AUTHOR]

Cryptanalysis has been studied and gradually improved with the evolution of cryptosystems over past decades. Recently, deep learning (DL) has started to be used in cryptanalysis to attack digital cryptosystems. As computing power keeps growing, deploying DL-based cryptanalysis becomes feasible in practice. However, since these studies can analyze only one cipher type for one DL model learning, it takes a lot of time to analyze multi ciphers. In this paper, we propose a unified cipher generative adversarial network (UC-GAN), which can perform ciphertext-to-plaintext translations among multiple domains (ciphers) using only a single DL model. In particular, the proposed model is based on unified unsupervised DL for the analysis of classical substitutional ciphers. Simulation results have indicated the feasibility and good performance of the proposed approach. In addition, we compared our experimental results with the findings of conditional GAN, where plaintext and ciphertext pairs in only the single domain are given as training data, and with CipherGAN, which is cipher mapping between unpaired ciphertext and plaintext in the single domain, respectively. The proposed model showed more than 97% accuracy by learning only data without prior knowledge of three substitutional ciphers. These findings could open a new possibility for simultaneously cracking various block ciphers, which has a great impact on the field of cryptography. To the best of our knowledge, this is the first study of the cryptanalysis of multiple cipher algorithms using only a single DL model [ABSTRACT FROM AUTHOR]