Di Zhao

In this paper, we propose a new geometric-shaping design for golden angle modulation (GAM) based on the complex geometric properties of open symmetrized bidisc, termed Bd-GAM, for future generation wireless communication systems. Inspired from the circular symmetric structure of the GAM, we construct the modulation schemes, Bd-GAM1 and Bd-GAM2. Specifically, we consider MI-optimized probabilistic modulation scheme with the geometrics properties of symmetric bidisc. With minimum SNR and entropy constraint, Bd-GAM1 and Bd-GAM2 can overcome the shaping-loss. Compared with the existed golden angle modulation introduced, the new design improves the mutual information, and the distance between adjacent constellation points.

In this paper, we propose a new geometric-shaping design for golden angle modulation (GAM) based on the complex geometric properties of open symmetrized bidisc, termed Bd-GAM, for future generation wireless communication systems. Inspired from the circular symmetric structure of the GAM, we construct the modulation schemes, Bd-GAM1 and Bd-GAM2. However, the optimization problem is hard to solve analytically, we consider this problem in the domain of symmetrized bidisc. Specifically, we joint the complex geometrics properties of symmetric bidisc and MI-optimized probabilistic modulation scheme. The symmetric bidisc is a bounded domain on which the Carathe´odory distance and the Kobayashi distance coincide. We can use the pseudo-metric mD in unit disc D to get the Kobayashi pseudo-distance and Carathe´odory pseudo-distance. With minimum SNR and entropy constraint, Bd-GAM1 and Bd-GAM2 can overcome the shaping-loss. This study finds that each constellation point of Bd-GAM has a unique phase and gain. Compared with the existed golden angle modulation introduced, the new design improves the mutual informaiton (MI), and the distance between adjacent constellation points.

In recent years, side-channel analysis based on deep learning has garnered significant attention from researchers. A pivotal reason for this lies in the fact that deep learning-based side-channel analysis requires minimal preprocessing of side-channel data. The automatic feature extraction property of deep learning methods drastically reduces the workload for researchers, enabling them to focus more on the core issues of side-channel analysis, namely, extracting sensitive information by attacking devices. However, in prior studies, most scholars have concentrated more on the model construction process, with little research focusing on the choice of optimizers.This paper explores a novel deep learning-based optimization algorithm—CPSGD (combined projection stochastic gradient descent). The algorithm comprises two variants, designed, respectively, for unprotected side-channel analysis (CPSGD1) and desynchronized side-channel analysis (CPSGD2), and their convergence has been theoretically proven. Experimental results demonstrate that, while maintaining the neural network structure unchanged, CPSGD1 exhibits the best performance on unprotected datasets compared to other publicly available optimizers, whereas CPSGD2 performs optimally on desynchronized datasets.

Cyber-attack activities are complex and ever-changing, posing severe challenges to cybersecurity personnel. Introducing knowledge graphs into the field of cybersecurity helps depict the intricate cybersecurity landscape and provides technical support for threat identification and situational awareness. However, during the process of constructing knowledge graphs, inevitable noise and conflicts may be introduced, leading to misleading inferences and decisions. This paper aims to address the issues of constructing and assessing the quality of cybersecurity knowledge graphs. We manually constructed a dataset of cybersecurity knowledge graphs (CS13K) and expanded the existing cybersecurity ontology, building a more versatile ontology applicable to existing data features. Building upon this, we utilized Neo4j to construct the cybersecurity knowledge graph. Furthermore, we propose an AttTucker model, based on Transformer, for assessing the quality of knowledge graphs. By employing numerous self-attention heads, this model captures latent information among entities and relations. While reducing the dimensionality of knowledge embeddings, the model achieves evaluation results comparable to high-dimensional embeddings. Finally, we incorporate the path-level information between entities in the knowledge graph into the evaluation of knowledge graph quality based on the AttTucker model. Experimental analysis on both generic datasets and cybersecurity datasets demonstrates that our model significantly improves F1 value and accuracy in knowledge graph quality assessment tasks, outperforming other models.