Abd Rahman Wahid^*

Journal of Cyber Security, Vol.7, pp. 589-614, 2025, DOI:10.32604/jcs.2025.071622 - 11 December 2025

Abstract The rapid increase in cyber attacks requires accurate, adaptive, and interpretable detection and response mechanisms. Conventional security solutions remain fragmented, leaving gaps that attackers can exploit. This study introduces the HI-XDR (Hybrid Intelligent Extended Detection and Response) framework, which combines network-based Suricata rules and endpoint-based Wazuh rules into a unified dataset containing 45,705 entries encoded into 1058 features. A semantic-aware autoencoder-based anomaly detection module is trained and strengthened through adversarial learning using Projected Gradient Descent, achieving a minimum mean squared error of 0.0015 and detecting 458 anomaly rules at the 99th percentile threshold. A comparative… More >

Jiming Lan¹, Hongwei Fu^1,*, Yadong Wu^1,2, Yaxian Liu^1,3, Jianhua Dong^1,2, Wei Liu^1,2, Huaqiang Chen^1,2

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4645-4665, 2025, DOI:10.32604/cmc.2025.067289 - 23 October 2025

Abstract In the research and production of fluorinated materials, large volumes of unstructured textual data are generated, characterized by high heterogeneity and fragmentation. These issues hinder systematic knowledge integration and efficient utilization. Constructing a knowledge graph for fluorinated materials processing is essential for enabling structured knowledge management and intelligent applications. Among its core components, Named Entity Recognition (NER) plays an essential role, as its accuracy directly impacts relation extraction and semantic modeling, which ultimately affects the knowledge graph construction for fluorinated materials. However, NER in this domain faces challenges such as fuzzy entity boundaries, inconsistent terminology,… More >

Priyadharsini Selvaraj^1,*, Senthil Kumar Jagatheesaperumal², Karthiga Marimuthu¹, Oviya Saravanan¹, Bader Fahad Alkhamees³, Mohammad Mehedi Hassan^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1575-1594, 2025, DOI:10.32604/cmes.2025.064138 - 30 May 2025

Abstract With expeditious advancements in AI-driven facial manipulation techniques, particularly deepfake technology, there is growing concern over its potential misuse. Deepfakes pose a significant threat to society, particularly by infringing on individuals’ privacy. Amid significant endeavors to fabricate systems for identifying deepfake fabrications, existing methodologies often face hurdles in adjusting to innovative forgery techniques and demonstrate increased vulnerability to image and video clarity variations, thereby hindering their broad applicability to images and videos produced by unfamiliar technologies. In this manuscript, we endorse resilient training tactics to amplify generalization capabilities. In adversarial training, models are trained using More >

Xuezhi Wen¹, Eric Danso^2,*, Solomon Danso²

Journal of Cyber Security, Vol.7, pp. 45-69, 2025, DOI:10.32604/jcs.2025.063606 - 08 May 2025

Abstract Deep learning models have achieved remarkable success in healthcare, finance, and autonomous systems, yet their security vulnerabilities to adversarial attacks remain a critical challenge. This paper presents a novel dual-phase defense framework that combines progressive adversarial training with dynamic runtime protection to address evolving threats. Our approach introduces three key innovations: multi-stage adversarial training with TRADES (Tradeoff-inspired Adversarial Defense via Surrogate-loss minimization) loss that progressively scales perturbation strength, maintaining 85.10% clean accuracy on CIFAR-10 (Canadian Institute for Advanced Research 10-class dataset) while improving robustness; a hybrid runtime defense integrating feature manipulation, statistical anomaly detection, and… More >

Yuqiao Liu¹, Chen Pan¹, YeonJae Oh^2,*, Chang Gyoon Lim^1,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4593-4629, 2025, DOI:10.32604/cmc.2025.061196 - 06 March 2025

Abstract Virtual Power Plants (VPPs) are integral to modern energy systems, providing stability and reliability in the face of the inherent complexities and fluctuations of solar power data. Traditional anomaly detection methodologies often need to adequately handle these fluctuations from solar radiation and ambient temperature variations. We introduce the Memory-Enhanced Autoencoder with Adversarial Training (MemAAE) model to overcome these limitations, designed explicitly for robust anomaly detection in VPP environments. The MemAAE model integrates three principal components: an LSTM-based autoencoder that effectively captures temporal dynamics to distinguish between normal and anomalous behaviors, an adversarial training module that… More >

Zhengshun Fei^1,*, Jianxin Chen¹, Gui Chen², Xinjian Xiang^1,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4237-4261, 2025, DOI:10.32604/cmc.2025.059262 - 06 March 2025

Abstract Tag recommendation systems can significantly improve the accuracy of information retrieval by recommending relevant tag sets that align with user preferences and resource characteristics. However, metric learning methods often suffer from high sensitivity, leading to unstable recommendation results when facing adversarial samples generated through malicious user behavior. Adversarial training is considered to be an effective method for improving the robustness of tag recommendation systems and addressing adversarial samples. However, it still faces the challenge of overfitting. Although curriculum learning-based adversarial training somewhat mitigates this issue, challenges still exist, such as the lack of a quantitative… More >

Qiuyu Lai^1,2, Wang Kang³, Lei Yang^1,2, Chun Yang^1,2,*, Delin Zhang^2,*

Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 649-664, 2024, DOI:10.32604/iasc.2024.051724 - 06 September 2024

Abstract Mathematical named entity recognition (MNER) is one of the fundamental tasks in the analysis of mathematical texts. To solve the existing problems of the current neural network that has local instability, fuzzy entity boundary, and long-distance dependence between entities in Chinese mathematical entity recognition task, we propose a series of optimization processing methods and constructed an Adversarial Training and Bidirectional long short-term memory-Selfattention Conditional random field (AT-BSAC) model. In our model, the mathematical text was vectorized by the word embedding technique, and small perturbations were added to the word vector to generate adversarial samples, while More >

Ruijin Xue^1,2, Shuang Feng^1,2,*, Qi Wang^1,2

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3107-3122, 2024, DOI:10.32604/cmc.2024.052600 - 15 August 2024

Abstract Users’ interests are often diverse and multi-grained, with their underlying intents even more so. Effectively capturing users’ interests and uncovering the relationships between diverse interests are key to news recommendation. Meanwhile, diversity is an important metric for evaluating news recommendation algorithms, as users tend to reject excessive homogeneous information in their recommendation lists. However, recommendation models themselves lack diversity awareness, making it challenging to achieve a good balance between the accuracy and diversity of news recommendations. In this paper, we propose a news recommendation algorithm that achieves good performance in both accuracy and diversity. Unlike… More >

Shuyi Li, Hongchao Hu^*, Xiaohan Yang, Guozhen Cheng, Wenyan Liu, Wei Guo

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2331-2359, 2024, DOI:10.32604/cmc.2024.047275 - 15 May 2024

Abstract Adversarial distillation (AD) has emerged as a potential solution to tackle the challenging optimization problem of loss with hard labels in adversarial training. However, fixed sample-agnostic and student-egocentric attack strategies are unsuitable for distillation. Additionally, the reliability of guidance from static teachers diminishes as target models become more robust. This paper proposes an AD method called Learnable Distillation Attack Strategies and Evolvable Teachers Adversarial Distillation (LDAS&ET-AD). Firstly, a learnable distillation attack strategies generating mechanism is developed to automatically generate sample-dependent attack strategies tailored for distillation. A strategy model is introduced to produce attack strategies that… More >

Kai Chen^1,2, Jinwei Wang³, James Msughter Adeke^1,2, Guangjie Liu^1,2,*, Yuewei Dai^1,4

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3247-3265, 2024, DOI:10.32604/cmc.2024.046082 - 26 March 2024

Abstract In recent years, various adversarial defense methods have been proposed to improve the robustness of deep neural networks. Adversarial training is one of the most potent methods to defend against adversarial attacks. However, the difference in the feature space between natural and adversarial examples hinders the accuracy and robustness of the model in adversarial training. This paper proposes a learnable distribution adversarial training method, aiming to construct the same distribution for training data utilizing the Gaussian mixture model. The distribution centroid is built to classify samples and constrain the distribution of the sample features. The… More >