Myoung-oh Choi¹, Mincheol Shin¹, Hyonjun Kang¹, Ka Lok Man², Mucheol Kim^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3191-3212, 2025, DOI:10.32604/cmes.2025.068723 - 30 September 2025

Abstract The escalating complexity and heterogeneity of modern energy systems—particularly in smart grid and distributed energy infrastructures—has intensified the need for intelligent and scalable security vulnerability classification. To address this challenge, we propose Vulnerability2Vec, a graph-embedding-based framework designed to enhance the automated classification of security vulnerabilities that threaten energy system resilience. Vulnerability2Vec converts Common Vulnerabilities and Exposures (CVE) text explanations to semantic graphs, where nodes represent CVE IDs and key terms (nouns, verbs, and adjectives), and edges capture co-occurrence relationships. Then, it embeds the semantic graphs to a low-dimensional vector space with random-walk sampling and skip-gram More >

Xiaohan Chen¹, Yuanfang Chen^1,*, Gyu Myoung Lee², Noel Crespi³, Pierluigi Siano⁴

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1733-1750, 2025, DOI:10.32604/cmc.2025.067284 - 29 August 2025

Abstract Graph Neural Networks (GNNs) have demonstrated outstanding capabilities in processing graph-structured data and are increasingly being integrated into large-scale pre-trained models, such as Large Language Models (LLMs), to enhance structural reasoning, knowledge retrieval, and memory management. The expansion of their application scope imposes higher requirements on the robustness of GNNs. However, as GNNs are applied to more dynamic and heterogeneous environments, they become increasingly vulnerable to real-world perturbations. In particular, graph data frequently encounters joint adversarial perturbations that simultaneously affect both structures and features, which are significantly more challenging than isolated attacks. These disruptions, caused… More >

Ho-Yin Wong¹, Guan-Yan Yang^1,*, Kuo-Hui Yeh², Farn Wang¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.32, No.1, pp. 1-4, 2024, DOI:10.32604/icces.2024.013279

Abstract 1 Introduction
This paper explores various strategies to enhance neural network performance, including adjustments to network architecture, selection of activation functions and optimizers, and regularization techniques. Hyperparameter optimization is a widely recognized approach for improving model performance [2], with methods such as grid search, genetic algorithms, and particle swarm optimization (PSO) [3] previously utilized to identify optimal solutions for neural networks. However, these techniques can be complex and challenging for beginners. Consequently, this research advocates for the use of SSO, a straightforward and effective method initially applied to the LeNet model in 2023 [4]. SSO optimizes… More >

S. Kalavathi^1,*, R. Nedunchelian²

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 39-56, 2023, DOI:10.32604/iasc.2023.031522 - 29 September 2022

Abstract Network analysis is a promising field in the area of network applications as different types of traffic grow enormously and exponentially. Reliable route prediction is a challenging task in the Large Scale Networks (LSN). Various non-self-learning and self-learning approaches have been adopted to predict reliable routing. Routing protocols decide how to send all the packets from source to the destination addresses across the network through their IP. In the current era, dynamic protocols are preferred as they network self-learning internally using an algorithm and may not entail being updated physically more than the static protocols.… More >

Yanyu Chen¹, Zhangjie Fu^1,2,*

Journal of Cyber Security, Vol.4, No.3, pp. 189-200, 2022, DOI:10.32604/jcs.2022.038401 - 01 February 2023

Abstract As a typical representative of web 2.0, Ethereum has significantly boosted the development of blockchain finance. However, due to the anonymity and financial attributes of Ethereum, the number of phishing scams is increasing rapidly and causing massive losses, which poses a serious threat to blockchain financial security. Phishing scam address identification enables to detect phishing scam addresses and alerts users to reduce losses. However, there are three primary challenges in phishing scam address recognition task: 1) the lack of publicly available large datasets of phishing scam address transactions; 2) the use of multi-order transaction information… More >