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

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

Quantum Neural Network based Distinguisher for Differential Cryptanalysis on Simplified Block Ciphers

Differential cryptanalysis is a block cipher analysis technology that infers a key by using the difference characteristics. Input differences can be distinguished using a good difference characteristic, and this distinguishing task can lead to key recovery. Artificial neural networks are a good solution for distinguishing tasks. For this reason, recently, neural distinguishers have been actively studied. We propose a distinguisher based on a quantum-classical hybrid neural network by utilizing the recently developed quantum neural network. To our knowledge, we are the first attempt to apply quantum neural networks for neural distinguisher. The target ciphers are simplified ciphers (S-DES, S-AES, S-PRESENT-[4]), and a quantum neural distinguisher that classifies the input difference from random data was constructed using the Pennylane library. Finally, we obtained quantum advantages in this work: improved accuracy and reduced number of parameters. Therefore, our work can be used as a quantum neural distinguisher with high reliability for simplified ciphers

Quantum Artificial Intelligence on Cryptanalysis

With the recent development of quantum computers, various studies on quantum artificial intelligence technology are being conducted. Quantum artificial intelligence can improve performance in terms of accuracy and memory usage compared to deep learning on classical computers. In this work, we proposed an attack technique that recovers keys by learning patterns in cryptographic algorithms by applying quantum artificial intelligence to cryptanalysis. Cryptanalysis was performed in the current practically usable quantum computer environment, and this is the world\u27s first study to the best of our knowledge. As a result, we reduced 70 epochs and reduced the parameters by 19.6%. In addition, higher average BAP (Bit Accuracy Probability) was achieved despite using fewer epochs and parameters. For the same epoch, the method using a quantum neural network achieved a 2.8% higher BAP with fewer parameters. In our approach, quantum advantages in accuracy and memory usage were obtained with quantum neural networks. It is expected that the cryptanalysis proposed in this work will be better utilized if a larger-scale stable quantum computer is developed in the future

Quantum Implementation of AIM: Aiming for Low-Depth

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 presents the first quantum circuit implementation of AIM and estimates its complexity (such as qubit count, gate count, and circuit depth) with respect to Grover\u27s search algorithm. For Grover\u27s key search, the most important optimization metric is the 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

Directly converted patient-specific induced neurons mirror the neuropathology of FUS with disrupted nuclear localization in amyotrophic lateral sclerosis

Background Mutations in the fused in sarcoma (FUS) gene have been linked to amyotrophic lateral sclerosis (ALS). ALS patients with FUS mutations exhibit neuronal cytoplasmic mislocalization of the mutant FUS protein. ALS patients fibroblasts or induced pluripotent stem cell (iPSC)-derived neurons have been developed as models for understanding ALS-associated FUS (ALS-FUS) pathology; however, pathological neuronal signatures are not sufficiently present in the fibroblasts of patients, whereas the generation of iPSC-derived neurons from ALS patients requires relatively intricate procedures. Results Here, we report the generation of disease-specific induced neurons (iNeurons) from the fibroblasts of patients who carry three different FUS mutations that were recently identified by direct sequencing and multi-gene panel analysis. The mutations are located at the C-terminal nuclear localization signal (NLS) region of the protein (p.G504Wfs*12, p.R495*, p.Q519E): two de novo mutations in sporadic ALS and one in familial ALS case. Aberrant cytoplasmic mislocalization with nuclear clearance was detected in all patient-derived iNeurons, and oxidative stress further induced the accumulation of cytoplasmic FUS in cytoplasmic granules, thereby recapitulating neuronal pathological features identified in mutant FUS (p.G504Wfs*12)-autopsied ALS patient. Importantly, such FUS pathological hallmarks of the patient with the p.Q519E mutation were only detected in patient-derived iNeurons, which contrasts to predominant FUS (p.Q519E) in the nucleus of both the transfected cells and patient-derived fibroblasts. Conclusions Thus, iNeurons may provide a more reliable model for investigating FUS mutations with disrupted NLS for understanding FUS-associated proteinopathies in ALS

Prevalence of Treated Epilepsy in Korea Based on National Health Insurance Data

The Korean national health security system covers the entire population and all medical facilities. We aimed to estimate epilepsy prevalence, anticonvulsant utilization pattern and the cost. We identified prevalent epilepsy patients by the prescription of anticonvulsants under the diagnostic codes suggesting seizure or epilepsy from 2007 Korean National Health Insurance databases. The information of demography, residential area, the kind of medical security service reflecting economic status, anticonvulsants, and the costs was extracted. The overall prevalence of treated epilepsy patients was 2.41/1,000, and higher for men than women. The age-specific prevalence was the lowest in those in their thirties and forties. Epilepsy was more prevalent among lower-income individuals receiving medical aid. The regional prevalence was the highest in Jeju Island and lowest in Ulsan city. New anticonvulsants were more frequently used than old anticonvulsants in the younger age group. The total annual cost of epilepsy or seizure reached 0.46% of total medical expenditure and 0.27% of total expenditure on health. This is the first nationwide epidemiological report issued on epilepsy in Korea. Epilepsy prevalence in Korea is comparable to those in developed countries. Economic status and geography affect the prevalence of epilepsy