A variational approach to the regularity of minimal surfaces of annulus type in Riemannian manifolds

Given two Jordan curves in a Riemannian manifold, a minimal surface of annulus type bounded by these curves is described as the harmonic extension of a critical point of some functional (the Dirichlet integral) in a certain space of boundary parametrizations. The $H^{2,2}$-regularity of the minimal surface of annulus type will be proved by applying the critical points theory and Morrey's growth condition.Comment: 22 pages. to appear in Differ. Geom. App

Phenanthroline diimide as an organic electron-injecting material for organic light-emitting devices

We report a diimide-type organic electron-injecting material, bis-[1,10]phenanthrolin-5-yl-pyromellitic diimide (Bphen-PMDI), for organic light-emitting devices (OLEDs), which was synthesized from its monomers, pyromellitic dianhydride (PMDA) and 1,10-phenanthrolin-5-amine (PTA). The vacuum-purified Bphen-PMDI powder showed high glass transition (∼230°C) and thermal decomposition (∼400°C) temperatures, whereas neither melting point nor particular long-range crystal nanostructures were observed from its solid samples. The optical band gap energy and the ionization potential of the Bphen-PMDI film were 3.6 eV and 6.0 eV, respectively, leading to the lowest unoccupied molecular orbital (LUMO) energy of 2.4 eV. Inserting a 1 nm thick Bphen-PMDI layer between the emission layer and the cathode layer improved the device current density by 10-fold and the luminance by 6-fold, compared to the OLED without the Bphen-PMDI layer. The result suggests that an effective electron tunnel injection process occurs through the Bphen-PMDI layer. © The Royal Society of Chemistry 2012.1

Grover on GIFT

Grover search algorithm can be used to find the $n$-bit secret key at the speed of $\sqrt{n}$, which is the most effective quantum attack method for block ciphers. In order to apply the Grover search algorithm, the target block cipher should be implemented in quantum circuits. Many recent research works optimized the expensive substitute layer to evaluate the need for quantum resources of AES block ciphers. Research on the implementation of quantum circuits for lightweight block ciphers such as SIMON, SPECK, HIGHT, CHAM, LEA, and Gimli, an active research field, is also gradually taking place. In this paper, we present optimized implementations of GIFT block ciphers for quantum computers. To the best of our knowledge, this is the first implementation of GIFT in quantum circuits. Finally, we estimate quantum resources for applying the Grover algorithm to the our optimized GIFT quantum circuit

Transformer encoder-based Crypto-Ransomware Detection for Low-Power Embedded Processors

Crypto-ransomware has a process to encrypt the victim\u27s files, and crypto-ransomware requests the victim for money for a key to decrypt the encrypted file. In this paper, we present new approaches to prevent crypto-ransomware by detecting block cipher algorithms for Internet of Things (IoT) platforms. The generic software of the AVR package and the lightweight block cipher library (FELICS) written in C language was trained through the neural network, and then we evaluated the result. Unlike the previous technique, the proposed method does not extract sequence and frequency characteristics, but considers opcodes and opcode sequences as words and sentences, performs word embedding, and then inputs them to the neural network based on the encoder structure of the transformer model. Through this approach, the file size was reduced by 0.5 times while maintaining a similar level of classification performance compared to the previous method. The detection success rate for the proposed method was evaluated with the F-measured value, which is the harmonic mean of precision and recall. In addition to achieving 98% crypto-ransomware detection success rates, classification by benign firmware and lightweight cryptography algorithm, Substitution-Permutation-Network (SPN) structure, Addition-Rotation-eXclusive-or structure (ARX) and normal firmware classification are also possible

Cryptanalysis of Caesar using Quantum Support Vector Machine

Recently, artificial intelligence-based cryptanalysis techniques have been researched. In this paper, we find the key of the Caesar cipher, which is a classical cipher, by using a quantum machine learning algorithm that learns by parameterized quantum circuit instead of a classical neural network. In the case of 4-bit plaintext and key, results could not be obtained due to the limitations of the cloud environment. But in the case of 2-bit plaintext and key, an accuracy of 1.0 was achieved, and in the case of 3-bit plaintext and key, an accuracy of 0.84 was achieved. In addition, as a result of cryptanalysis for a 2-bit dataset on IBM\u27s real quantum processor, a classification accuracy of 0.93 was achieved. In the future, we will research a qubit reduction method for cryptanalysis of longer-length plaintext and key, and a technique for maintaining accuracy in real quantum hardware

Efficient Arithmetic on ARM-NEON and Its Application for High-Speed RSA Implementation

Advanced modern processors support Single Instruction Multiple Data (SIMD) instructions (e.g. Intel-AVX, ARM-NEON) and a massive body of research on vector-parallel implementations of modular arithmetic, which are crucial components for modern public-key cryptography ranging from RSA, ElGamal, DSA and ECC, have been conducted. In this paper, we introduce a novel Double Operand Scanning (DOS) method to speed-up multi-precision squaring with non-redundant representations on SIMD architecture. The DOS technique partly doubles the operands and computes the squaring operation without Read-After-Write (RAW) dependencies between source and destination variables. Furthermore, we presented Karatsuba Cascade Operand Scanning (KCOS) multiplication and Karatsuba Double Operand Scanning (KDOS) squaring by adopting additive and subtractive Karatsuba\u27s methods, respectively. The proposed multiplication and squaring methods are compatible with separated Montgomery algorithms and these are highly efficient for RSA crypto system. Finally, our proposed multiplication/squaring, separated Montgomery multiplication/squaring and RSA encryption outperform the best-known results by 22/41\%, 25/33\% and 30\% on the Cortex-A15 platform

Deep Learning based Cryptanalysis of Lightweight Block Ciphers, Revisited

Cryptanalysis is to infer the secret key of cryptography algorithm. There are brute-force attack, differential attack, linear attack, and chosen plaintext attack. With the development of artificial intelligence, deep learning-based cryptanalysis has been actively studied. There are works in which known-plaintext attacks against lightweight block ciphers, such as S-DES, have been performed. In this paper, we propose a cryptanalysis method based on the-state-of-art deep learning technologies (e.g. residual connections and gated linear units) for lightweight block ciphers (e.g. S-DES and S-AES). The number of parameters required for training is significantly reduced by 93.16~\% and the average of bit accuracy probability increased by about 5.3~\%, compared with previous work

LEA Block Cipher in Rust Language: Trade-off between Memory Safety and Performance

Cryptography implementations of block cipher have been written in C language due to its strong features on system-friendly features. However, the C language is prone to memory safety issues, such as buffer overflows and memory leaks. On the other hand, Rust, novel system programming language, provides strict compile-time memory safety guarantees through its ownership model. This paper presents the implementation of LEA block cipher in Rust language, demonstrating features to prevent common memory vulnerabilities while maintaining performance. We compare the Rust implementation with the traditional C language version, showing that while Rust incurs a reasonable memory overhead, it achieves comparable the execution timing of encryption and decryption. Our results highlight Rust’s suitability for secure cryptographic applications, striking the balance between memory safety and execution efficiency

Analysis of Parallel Implementation of Pilsung Block Cipher On Graphics Processing Unit

This paper focuses on the GPU implementation of the Pilsung block cipher used in the Red Star 3.0 operating system developed in North Korea. The Pilsung block cipher is designed based on AES. One notable feature of the Pilsung block cipher is that the table calculations required for encryption take longer than the encryption process itself. This paper emphasizes the parallel implementation of the Pilsung block cipher by leveraging the parallel processing capabilities of GPUs and evaluates the performance of the Pilsung block cipher. Techniques for optimization are proposed, including the use of Pinned memory to reduce data transfer time and work distribution between the CPU and GPU. Pinned memory helps optimize data transfer, and work distribution between the CPU and GPU needs to be considered for efficient parallel processing. Performance measurements were performed using the Nvidia GTX 3060 laptop for evaluation, comparing the results of applying Pinned memory usage and work distribution optimization. As a result, optimizing memory transfer costs was found to have a greater impact on performance improvement. When both techniques were applied together, approximately a 1.44 times performance improvement was observed