Semantic communication has emerged as a promising technique that can integrate the meaning and context of information into a communication process. It goes beyond raw data transmission and enables a sender and a receiver to transmit and recover semantic messages through their respective knowledge bases. However, it is challenging to achieve secure semantic communication due to complicated security and privacy threats in semantic communication. In this article, we first introduce the background and architecture of semantic communication. Then, we sort out multiple potential security threats existing in each step of semantic communication. Following that, we summarize the requirements for secure semantic communication and provide possible defenses for each type of attacks. We also present a case study to illustrate the training process of a reliable semantic encoder/decoder. Finally, we discuss open issues and future directions in this research field.

The concepts between Internet of Things (IoT) and edge computing are increasingly intertwined, as an edge-computing architecture generally comprises a (large) number of diverse IoT devices. This, however, increases the potential attack vectors since any one of these connected IoT devices can be targeted to facilitate other malicious cyber activities. Physical attacks are generally harder to mitigate and less studied, in comparison to their cyber counterparts. Thus, in this article we present an attack framework targeting true random number generators (TRNGs), which are a key component in cryptosystems for edge devices. We then demonstrate how such a framework can guide our investigation of a commercial ASIC chip that runs ring-oscillator-based TRNG. Specifically, we show that our template power attack, low voltage fault attack, and voltage glitch fault attack do not require prior knowledge of the TRNG implementation.

Various Artificial Intelligence (AI) techniques are combined with classic side-channel methods to improve the efficiency of attacks. Among them, Genetic-Algorithms-based Correlation Power Analysis (GA-CPA) is proposed to launch attacks on hardware cryptosystems to extract the secret key efficiently. However, the convergence efficiency of GA-CPA is unsatisfactory due to two problems: the randomly generated initial population generally have low fitness, and the mutation operation in each iteration hardly produces high-quality individuals because of the confusion and diffusion characteristics of S-boxes. In this paper, we propose an analysis framework of GA-CPA which focuses on solving these two problems. First, we explore the list of candidate key bytes which is the result of Correlation Power Analysis (CPA) on a limited number of power traces, so that the population can be initialized with high quality candidates. Second, we improve the mutation operation by guiding the candidate key to mutate in a higher-fitness direction instead of randomly. Third, we make full use of the fitness calculation method and combine it with key enumeration algorithms to further improve the efficiency of key recovery. Simulation experimental results show that our method reduces the number of traces by 33.3% and 43.9% compared to CPA with key enumeration and GA-CPA respectively when the success rate is fixed to 90%. Real experiments performed on SAKURA-G confirm that the number of traces required in our method is much less than the numbers of traces required in CPA and GA-CPA. Besides, we adjust our method to deal with DPA contest v1 dataset, and achieve a better result of 40.76 traces than the winning proposal of 42.42 traces. The computation cost of our proposal is nearly 16.7% of the winner.

Edge computing handles delay-sensitive data and provides real-time feedback, while it brings data security issues to edge devices (such as IoT devices and edge servers). Side-channel attacks are main threatens to these devices. Collision attack represents a powerful category of side-channel analysis in extracting security information from embedded cryptographic algorithms. Since its proposition in 2003, plenty collision detection algorithms are presented, most of which enumerate all the values of target plaintext byte to find a collision. In this paper, we establish a relation between “Euclidean distance between traces” and “Hamming distance between values”, and take advantage of the distance information leaked from the power traces of encrypting an adaptively chosen plaintext to reduce the candidate plaintext space. Consequently, the collision is detected at a high pace. Moreover, this improvement is fault-tolerant, and its self-correction feature promotes the efficiency of attacks based on our method significantly. We take AES implemented with masks, which is usually employed in edge computing devices, for instance to introduce our method and conduct experiments to verify its efficiency. According to the experimental results, for whole key recovery attacks, our method requires only 26.5% plaintexts, 32.2% traces, and much less than 10% computations of the collision-correlation attack launched by Clavier et al.

Side-channel analysis (SCA) based on machine learning, particularly neural networks, has gained considerable attention in recent years. However, previous works predominantly focus on establishing connections between labels and related profiled traces. These approaches primarily capture label-related features and often overlook the connections between traces of the same label, resulting in the loss of some valuable information. Besides, the attack traces also contain valuable information that can be used in the training process to assist model learning. In this paper, we propose a profiled SCA approach based on contrastive learning named CL-SCA to address these issues. This approach extracts features by emphasizing the similarities among traces, thereby improving the effectiveness of key recovery while maintaining the advantages of the original SCA approach. Through experiments of different datasets from different platforms, we demonstrate that CL-SCA significantly outperforms other approaches. Moreover, by incorporating attack traces into the training process using our approach, we can further enhance its performance. This extension can improve the effectiveness of key recovery, which is fully verified through experiments on different datasets.