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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. [ABSTRACT FROM AUTHOR]

Device-to-device (D2D) communication is a direct means of communication between devices without an intermediate node, and it helps to expand cell coverage and to increase radio frequency reuse in a 5G network. Moreover, D2D communication is a core technology of 5G vehicle-to-everything (V2X) communication, which is an essential technology for autonomous driving. However, typical D2D communication in an 4G network which is typical telecommunication network has various security challenges including impersonation, eavesdropping, privacy sniffing, free-riding attack, etc. Moreover, when IoT technology emerges with 5G networks in massive machine type communication (mMTC) and ultra-reliable low latency communication (URLLC) application scenarios, these security challenges are more crucial and harder to mitigate because of the resource-constrained nature of IoT devices. To solve the security challenges in a 5G IoT environment, we need a lightweight and secure D2D communication system that can provide secure authentication, data confidentiality/integrity and anonymity. In this paper, we survey and analyze existing results about secure D2D communication systems in terms of their security considerations and limitations. Then, we lastly propose a secure D2D communication system to address the aforementioned security challenges and the limitations of the existing results. The proposed secure D2D communication was designed based on elliptic curve cryptography (ECC) and lightweight authenticated encryption with associated data (AEAD) ciphers to cover resource-constrained IoT devices. [ABSTRACT FROM AUTHOR]