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

DeepDetection: Privacy-Enhanced Deep Voice Detection and User Authentication for Preventing Voice Phishing

The deep voice detection technology currently being researched causes personal information leakage because the input voice data are stored in the detection server. To overcome this problem, in this paper, we propose a novel system (i.e., DeepDetection) that can detect deep voices and authenticate users without exposing voice data to the server. Voice phishing prevention is achieved in two-way approaches by performing primary verification through deep voice detection and secondary verification of whether the sender is the correct sender through user authentication. Since voice preprocessing is performed on the user local device, voice data are not stored on the detection server. Thus, we can overcome the security vulnerabilities of the existing detection research. We used ASVspoof 2019 and achieved an F1-score of 100% in deep voice detection and an F1 score of 99.05% in user authentication. Additionally, the average EER for user authentication achieved was 0.15. Therefore, this work can be effectively used to prevent deep voice-based phishing

Malicious Contract Detection for Blockchain Network Using Lightweight Deep Learning Implemented through Explainable AI

A smart contract is a digital contract on a blockchain. Through smart contracts, transactions between parties are possible without a third party on the blockchain network. However, there are malicious contracts, such as greedy contracts, which can cause enormous damage to users and blockchain networks. Therefore, countermeasures against this problem are required. In this work, we propose a greedy contract detection system based on deep learning. The detection model is trained through the frequency of opcodes in the smart contract. Additionally, we implement Gredeeptector, a lightweight model for deployment on the IoT. We identify important instructions for detection through explainable artificial intelligence (XAI). After that, we train the Greedeeptector through only important instructions. Therefore, Greedeeptector is a computationally and memory-efficient detection model for the IoT. Through our approach, we achieve a high detection accuracy of 92.3%. In addition, the file size of the lightweight model is reduced by 41.5% compared to the base model and there is little loss of accuracy

Quantum Neural Network Based Distinguisher on SPECK-32/64

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