Lightweight Block Research Articles

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.

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.

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.

Parallel lightweight Block Cipher algorithm for Multicore CPUs

أصبحت حماية البيانات واحدة من أهم القضايا على الرغم من التقدم الكبير في الاتصالات والتكنولوجيا. لكي ترسل التكنولوجيا المستندة إلى الويب البيانات بسرعة وأمان ، يجب تشفير البيانات. التشفير هو عملية تحويل النص العادي إلى نص مشفر ، لا يستطيع الأشرار قراءته أو تغييره. تتطلب كل من إجراءات تحليل التشفير وفك التشفير قدرًا كبيرًا من الوقت من أجل الحفاظ على المستوى المطلوب من الأمان. ومع ذلك ، طور عدد من الباحثين نهج التشفير بالتوازي من أجل تقليل مقدار الوقت اللازم لإنهاء إجراءات التشفير وفك التشفير. أنتج التحقيق في هذه القضية عددًا من الحلول القابلة للتطبيق. تمكن الباحثون من تحقيق مستويات أداء محسّنة في تقنية التشفير باستخدام التوازي لزيادة الإنتاجية وتعزيز كفاءة طرق التشفير. لتحقيق أداء عالٍ ، تم تقديم خوارزميات تشفير البقعة خفيفة الوزن وتنفيذها على منصات وحدة المعالجة المركزية مع تحسينات مختلفة. وبالتالي ، في هذا العمل ، تم اقتراح مخطط تشفير خفيف الوزن يستخدم جولة واحدة فقط من تقنية تشفير الكتلة التي يتم تطبيقها بالتوازي على معالج متعدد النواة. تستخدم خوارزمية تشفير الرسائل المقترحة كتلتين فرعيتين من 128 بت من الرسائل العادية ومربع الاستبدال و splitmix64 PRNG لتشفير الرسالة العادية والحصول على كتلتين فرعيتين مشفرتين ، مما يجعلها تقنية سريعة لتشفير وفك تشفير كتل الرسائل. بالمقارنة مع الطريقة الحالية. وفقًا لنتائج الأداء ، فإنه قادر على الوصول إلى إنتاجية عالية للبيانات مقارنة ببعض الأساليب خفيفة الوزن الموجودة بالفعل ، مع معدل نقل أعلى من 25 كيكابت في الثانية على وحدة المعالجة المركزية Intel Core i7. تتفوق طريقة التشفير المقترحة على طريقة البقع المتوازية بمعدل 14.10 مرة أسرع عند تنفيذها على وحدة معالجة مركزية متعددة النواة. متوسط التسريع مقارنة بالنسخة التسلسلية للخوارزمية المقترحة والتنفيذ المتوازي لها هو 4.70. أيضًا ، توفر طريقة التشفير المقترحة قدرًا كبيرًا من العشوائية وتجتاز الاختبارات الإحصائية لـ PractRand. وبالتالي ، فإن الطريقة المقترحة هي منافس قوي للتنفيذ عالي الأمان على المعالجات متعددة النواة.

Assessment of heavy metal concentrations in blood and radon concentrations in urine samples of workers at selected building material factories in Erbil, Iraq

ABSTRACT A total of 90 blood and urine samples were assessed to evaluate the health risks associated with radon and heavy metals in the Erbil Governorate. The worker group from the building materials manufacturers provided 70 samples, while an additional 20 samples were obtained from a control group. X-ray fluorescence technology was employed to measure and analyse the heavy metal content in the collected blood samples, while radon levels in the urine samples were analysed using a CR-39 detector. The findings indicate that the working group exhibits greater levels of radon and heavy components (Pb, Cd, and Ni) compared to the control group. The statistical analysis revealed that the levels of radon in urine samples of workers were significantly different (p ≤ 0.001) in certain factories, such as the cement plant, lightweight block, and red brick factories, compared to the control group. However, the levels were not statistically significant in other factories. The test showed significant differences in Pb concentrations between the employed and control groups (p < 0.05), with higher average values of Cd and Ni, but not statistically significant (P ˃ 0.05). The study found a positive correlation between the duration of employment and the average concentration of heavy metals (Pb, Cd, and Ni) in workers’ blood samples. Additionally, smokers and non-smokers had significantly different mean heavy element levels in their blood samples (p < 0.05). There is no statistically significant correlation between smoking status and radon levels (P ˃ 0.05). Remarkably, each urine sample had radon levels below the 200 Bq/m3 International Atomic Energy Agency standard, suggesting that urine is impurity-free. These results are critical and serve as baseline data in the Kurdistan region.

Multidimensional zero-correlation linear cryptanalysis on uBlock and RAIN

uBlock and RAIN are both SPN structure lightweight block ciphers with sufficient security against traditional attacks. This article mainly focuses on the zero-correlation linear cryptanalysis of uBlock-128/128 and RAIN-64 without considering the whitening key for the first time. On the one hand, combining the linear mask propagation rules of S-box with matrix method, a large number of 4-round zero-correlation linear approximations for uBlock-128/128 are obtained. Therefore, a 6-round key recovery attack is carried out with partial-compression technique, which can recover 48-bit subkeys with data complexity 2 126.41 known plaintexts, time complexity 2 122.82 times of 6-round of algorithm encryptions, and memory complexity 2 48 nibbles. On the other hand, a similar method is used on RAIN-64, thus a 10-round attack is performed, which recovers 64-bit subkeys with data complexity 2 61.95 known plaintexts, time complexity 2 74.26 times of 10-round algorithm encryptions, and memory complexity 2 64 nibbles. The results show that both uBlock and RAIN are safe enough to resist zero-correlation linear cryptanalysis.

MILP Based Differential Cryptanalysis on IVLBC and Eslice 64

Lightweight block ciphers provide security to resource-limited devices. However, many of these ciphers lack security analysis against basic attacks. This paper provides a detailed security analysis of two lightweight block ciphers, IVLBC and Eslice-64, against differential attack. The designers of IVLBC and Eslice-64 claimed that their ciphers were secure against differential attack. In this paper, to substantiate existing cryptanalysis’s claims, we perform differential attack on these two ciphers using the mixed-integer linear programming (MILP) method. We incorporate the difference distribution table (DDT) probabilities into MILP models. We discover differential distinguishers up to seven and 15 rounds for IVLBC and Eslice-64, respectively. We improve the known distinguishers for Eslice-64 by one round. Further, we mount the key recovery attack on an eight-round IVLBC and a 16-round Eslice-64 with data/memory/time complexities of 249/ 250.59/249 and 263/212.58/263 respectively.

INLEC: An involutive and low energy lightweight block cipher for internet of things

The Internet of Things (IoT) has emerged as a pivotal force in the global technological revolution and industrial transformation.Despite its advancements, IoT devices continue to face significant security challenges, particularly during data transmission, and are often constrained by limited battery life and energy resources.To address these challenges,a low energy lightweight block cipher (INLEC) is proposed to mitigate data leakage in IoT devices. In addition, the Structure and Components INvolution (SCIN) design is introduced. It is constructed using two similar round functions to achieve front–back symmetry.This design ensures coherence throughout the INLEC encryption and decryption processes and addresses the increased resource consumption during the decryption phase in Substitution Permutation Networks (SPN).Furthermore, a low area S-box is generated through a hardware gate-level circuit search method combined with Genetic Programming (GP).This optimization leads to a 47.02% reduction in area compared to the S0 of Midori, using UMC 0.18μm technology. Moreover, a chaotic function is used to generate the optimal nibble-based involutive permutation, further enhancing its efficiency.In terms of performs, the energy consumption for both encryption and decryption with INLEC is 6.88 μJ/bit, representing 25.21% reduction compared to Midori.Finally, INLEC is implemented using STM32L475 PanDuoLa and Nexys A7 FPGA development boards, establishing an encryption platform for IoT devices. This platform provides functions for data acquisition, transmission, and encryption.

GFLE: a low-energy lightweight block cipher based on a variant of generalized Feistel structure

Low-energy lightweight block ciphers are essential for applications with extremely resource-constrained to reduce energy and maintain security. The trade-off between diffusion property and area is a widely studied issue in the design of low-energy block ciphers. In this paper, a low-energy lightweight block cipher named as GFLE is presented. The core cipher of GFLE uses a variant of the Generalized Feistel Structure (GFS) with 4-branch, which combines the Type-II GFS with the simplified Lai-Massey. The DRmax of GFLE has a one-round improvement over the Type-II GFS optimized by Suzaki et al and the security margin is achieved in a shorter number of rounds. Moreover, an S-box with low-energy and good cryptographic properties is proposed by searching combinations based on gate-level circuits using a depth-first strategy. It exhibits better security properties and hardware performance compared to other S-boxes. The block cipher GFLE is implemented in ASIC with UMC 0.18 μm. It has been proved that the energy of GFLE is lower than Midori, WARP, SKINNY, CRAFT, etc in unified encryption and decryption (ED) circuits. GFLE reduces energy by 61.59% compared with SKINNY. The results show that GFLE in ED circuits consumes only 1596 Gate Equivalents (GEs) and 6.36 μ J/bit in area and energy, respectively.

Search for high-probability differential characteristics of the lightweight block cipher algorithm present with non-standard substitution blocks

The development of the Internet of Things and the associated devices has made it necessary to establish and implement encryption standards to ensure secure data transmission. These standards need to be comply with fundamental encryption principles and cater to devices with limited computational resources. As a result, lightweight cryptography has emerged as a distinct field within cryptography. The PRESENT block cipher algorithm is a lightweight encryption algorithm designed for deployment in resource-constrained devices. It requires comprehensive and ongoing vulnerability analysis against both known and novel cryptanalysis methods. This work extensively investigates the PRESENT block cipher algorithm, examining its components, operational principles, and key scheduling algorithm. This study analyses existing research on the algorithm with regards to contemporary cryptanalysis methods. Differential cryptanalysis was selected as the method of choice. The requirements for constructing S-boxes, as set forth by the algorithm developers, are reviewed. Two alternative S-boxes are formulated and presented based on these requirements. The paper presents a methodology for identifying high-probability differential characteristics for the PRESENT algorithm, using a substitute substitution block that differs from the one proposed by the developers. The research reports on the encryption algorithm PRESENT, using alternative substitution blocks, and evaluates its resistance to differential cryptanalysis. The text presents the results of applying the methodology for searching differential characteristics to the substituted blocks in the PRESENT algorithm. A comparative analysis is made between the results obtained through the differential characteristic search methodology for the PRESENT algorithm with alternative substitution blocks and the known results for this algorithm.

Research on Fault Attacks of Lightweight Cryptographic Algorithms

Fault attacks exploit fault injection techniques to compromise cryptographic systems, compromising device operations and leaking secret keys. Lightweight block cipher algorithms are suitable for resource-constrained environments, ensuring security while reducing hardware and software overhead. This paper summarizes the current research status of fault attacks on some lightweight cryptographic algorithms, including block ciphers, stream ciphers, etc. These studies are crucial for addressing system vulnerabilities, protecting personal privacy, and enhancing the stability of embedded systems. The aim of this paper is to provide comprehensive references for cryptographers, assisting in selecting suitable algorithms and defense strategies.

Security Evaluation of Lightweight Block Ciphers Against Mixture Differential Cryptanalysis

Security Evaluation of Lightweight Block Ciphers Against Mixture Differential Cryptanalysis

RAB: A lightweight block cipher algorithm with variable key length

RAB: A lightweight block cipher algorithm with variable key length

Utilization of waste phosphogypsum and absorbent polymer in preparing hollow particles and its application in lightweight blocks

In prefabricated interior wall construction, it is necessary to reduce the material weight as much as possible without sacrificing the strength. This study proposes a novel method by shelling spherical absorbent polymer (SAP) with building phosphogypsum (BPG) to manufacture hollow particles and the associated lightweight block. The apparent density, unconfined compressive strength (UCS) and hydration heat of lightweight blocks were measured for comparative analysis, the water release mechanism of the SAP-based hollow particles was revealed by mercury intrusion porosimetry (MIP) and scanning electron microscope (SEM) tests. The results indicated that the inclusion of hollow particles reduced the apparent density of the blocks by 23.68% and 14.41% for OPC and BPG, respectively. BPG hollow particle is more effective in speeding up the block's early strength than OPC counterpart. In addition, both unshelled and shelled SAP-based blocks can delay the occurrence of the heat release peak, while the latter exhibits better mechanical properties and geometric stability than the former. SAP particles can serve as a water reservoir to facilitate internal curing, which is beneficial to the weight reduction, strength formation, and heat releasing of blocks. Microscopic tests indicated that the fraction of large pores increased while the small pores decreased in shelled hollow particle block compared with pure OPC block. Beyond which, it was noticed that the needle-shaped ettringite tends to generate between the interface of shelled hollow particle and OPC matrix, which plays a positive role in the overall strength of the block. The proposed SAP-based hollow particle block can not only reduce the overall building weight but also promotes the efficient utilization of waste phosphogypsum, resulting in both economic and environmental benefits.

Novel Substitution Box Architectural Synthesis for Lightweight Block Ciphers

This paper proposes three novel Substitution box (S-box) architectures for lightweight block ciphers. The proposed S-box architectures are based on Boolean decomposition to achieve the design tradeoffs. Absorption of the Terminal Nodes property, positive Davio expansion, and Shannon decomposition are explored for the three S-box architectures. The proposed S-box variants are included in the SPN layer of the Midori-64 block cipher to validate their utility for a lightweight cipher. Also, to demonstrate the generality of the proposed logic decomposition approaches, the proposed work is expanded to the composite field architectures for the S-box of AES-128. Compared with state-of-the-art designs, the proposed designs exhibit a reduction of 66%, 36%, and 21% for energy, delay, and power metrics.

Machine learning-based lightweight block ciphers for resource-constrained internet of things networks: a review

The increasing number of internet of things (IoT) devices, wearable technologies, and embedded systems has experienced a significant increase in recent years. This surge has brought attention to the necessity for cryptographic algorithms that are lightweight and capable of providing security in resource-constrained environments. The primary objective of lightweight block ciphers is to provide encryption capabilities with minimal computational overhead and decreased power consumption. As a result, they are particularly well-suited for use on devices that have limited resources. At the same time, machine learning methodologies have evolved into powerful mechanisms for the purposes of prediction, categorization, and system optimization. This study introduces a challenges and issues involved in integrating machine learning techniques with the development of lightweight block ciphers.

LPHD: A low power and high diffusion lightweight block cipher

AbstractSmart door locks pose a large number of threats such as network attacks. Its storage area and power of cipher are severely limited because the wireless nodes of smart door locks are mostly battery‐powered. Therefore, effective security solutions are urgently needed. In this paper, a new lightweight block cipher with low power named LPHD is proposed to ensure the security of the master control chip of the smart door lock terminal. We design a scheme of low power S‐box and construct the two‐stage permutation layer (TP structure) suitable for LPHD by filtering the sets of 8‐bit permutations. LPHD proposes a variant of the 8‐branch generalized Feistel structure (GFS) to realize that the bits of all branches are affected in one encryption round. The problem of slow diffusion in the standard Feistel structure is solved. The key schedule adopts the nonlinear design and reuses the encryption process of LPHD. It improves the security of the cipher and reduces hardware overhead. Moreover, we evaluate the hardware implementation and security of LPHD. The results show that LPHD for the unified encryption and decryption circuits requires only 1276 Gate Equivalents (GEs) and 1.914 W on UMC 0.18 m, which is better than other lightweight block ciphers such as SKINNY, PRESENT, and IVLBC. In summary, LPHD provides sufficient security for the master control chip of the smart door lock terminal.

Mixture Differential Cryptanalysis on Round-Reduced SIMON32/64 Using Machine Learning

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.

LTLBC: a low-latency lightweight block cipher for internet of things

LTLBC: a low-latency lightweight block cipher for internet of things

IoVCipher: A low-latency lightweight block cipher for internet of vehicles

The data security of CAN bus system is receiving increasing attention with the rapid development of Internet of Vehicles (IoV). However, traditional ciphers are not the best choice due to the limitations of computation, real-time, and resources of Electronic Control Units in vehicles. Thus, this paper proposes a lightweight block cipher IoVCipher to protect the security of IoV. It is designed focus on the latency and area in round-based architectures (both encryption and decryption) to meet this resource-constrained environments. For this purpose, two S-boxes with low latency and tiny area are constructed in this paper, one involution and one non-involution. Considering the decryption latency, a low latency subkey generation method is designed. In addition, this paper proposes a new extended MISTY structure that makes the encryption and decryption of hardware implementations similar. In comparison to other low-latency lightweight block ciphers such as PRINCE, QARMA, MANTIS and LLLWBC, IoVCipher achieves an effective balance between latency and area in the round-based architecture, and IoVCipher has low latency, low area, and low energy in the fully unrolled architecture. Finally, IoVCipher is implemented on a real-time speed acquisition and encryption testbed to simulate encrypted transmission of real-time speed in a CAN bus environment.