Chunyong Yin, Williams Kyei^*

Journal of Cyber Security, Vol.7, pp. 615-635, 2025, DOI:10.32604/jcs.2025.072740 - 24 December 2025

Abstract The evolving sophistication of network threats demands anomaly detection methods that are both robust and adaptive. While autoencoders excel at learning normal traffic patterns, they struggle with complex feature interactions and require manual tuning for different environments. We introduce the Adaptive Robust AutoEncoder (ARAE), a novel framework that dynamically balances reconstruction fidelity with latent space regularization through learnable loss weighting. ARAE incorporates multi-head attention to model feature dependencies and fuses multiple anomaly indicators into an adaptive scoring mechanism. Extensive evaluation on four benchmark datasets demonstrates that ARAE significantly outperforms existing autoencoder variants and classical methods, More >

Baolin Li¹, Tao Hu^1,2,3,*, Xinlei Liu¹, Jichao Xie¹, Peng Yi^1,2,3

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 2141-2155, 2025, DOI:10.32604/cmc.2025.066370 - 29 August 2025

Abstract Deep neural networks are known to be vulnerable to adversarial attacks. Unfortunately, the underlying mechanisms remain insufficiently understood, leading to empirical defenses that often fail against new attacks. In this paper, we explain adversarial attacks from the perspective of robust features, and propose a novel Generative Adversarial Network (GAN)-based Robust Feature Disentanglement framework (GRFD) for adversarial defense. The core of GRFD is an adversarial disentanglement structure comprising a generator and a discriminator. For the generator, we introduce a novel Latent Variable Constrained Variational Auto-Encoder (LVCVAE), which enhances the typical beta-VAE with a constrained rectification module… More >

An-Chao Tsai^*, Chayanon Pookunngern

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2455-2471, 2025, DOI:10.32604/cmc.2025.067413 - 03 July 2025

Abstract Food waste presents a major global environmental challenge, contributing to resource depletion, greenhouse gas emissions, and climate change. Black Soldier Fly Larvae (BSFL) offer an eco-friendly solution due to their exceptional ability to decompose organic matter. However, accurately identifying larval instars is critical for optimizing feeding efficiency and downstream applications, as different stages exhibit only subtle visual differences. This study proposes a real-time mobile application for automatic classification of BSFL larval stages. The system distinguishes between early instars (Stages 1–4), suitable for food waste processing and animal feed, and late instars (Stages 5–6), optimal for… More >

Tian Zhao^*, Jun Tu^*, Puzheng Quan, Ruisheng Xiong

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1351-1377, 2025, DOI:10.32604/cmc.2025.063209 - 09 June 2025

Abstract Aiming at the problems of incomplete characterization of text relations, poor guidance of potential representations, and low quality of model generation in the field of controllable long text generation, this paper proposes a new GSPT-CVAE model (Graph Structured Processing, Single Vector, and Potential Attention Computing Transformer-Based Conditioned Variational Autoencoder model). The model obtains a more comprehensive representation of textual relations by graph-structured processing of the input text, and at the same time obtains a single vector representation by weighted merging of the vector sequences after graph-structured processing to get an effective potential representation. In the… More >

Chunhao Zhang^1,2, Bin Xie^2,3,*, Zhibin Huo¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 843-860, 2025, DOI:10.32604/cmc.2025.063151 - 09 June 2025

Abstract Time series anomaly detection is crucial in finance, healthcare, and industrial monitoring. However, traditional methods often face challenges when handling time series data, such as limited feature extraction capability, poor temporal dependency handling, and suboptimal real-time performance, sometimes even neglecting the temporal relationships between data. To address these issues and improve anomaly detection performance by better capturing temporal dependencies, we propose an unsupervised time series anomaly detection method, VLT-Anomaly. First, we enhance the Variational Autoencoder (VAE) module by redesigning its network structure to better suit anomaly detection through data reconstruction. We introduce hyperparameters to control… More >

Jianxin Feng^1,2,*, Xiaoyao Liu^1,2

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2087-2107, 2025, DOI:10.32604/cmc.2025.060252 - 16 April 2025

Abstract As a form of discrete representation learning, Vector Quantized Variational Autoencoders (VQ-VAE) have increasingly been applied to generative and multimodal tasks due to their ease of embedding and representative capacity. However, existing VQ-VAEs often perform quantization in the spatial domain, ignoring global structural information and potentially suffering from codebook collapse and information coupling issues. This paper proposes a frequency quantized variational autoencoder (FQ-VAE) to address these issues. The proposed method transforms image features into linear combinations in the frequency domain using a 2D fast Fourier transform (2D-FFT) and performs adaptive quantization on these frequency components… More >

Shuqiao Liu¹, Zhao Zhang^2,*, Hongyan Zhou¹, Xuebo Chen¹

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 929-948, 2025, DOI:10.32604/cmc.2024.057319 - 03 January 2025

Abstract This study examines the effectiveness of artificial intelligence techniques in generating high-quality environmental data for species introductory site selection systems. Combining Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis data with Variation Autoencoder (VAE) and Generative Adversarial Network (GAN) the network framework model (SAE-GAN), is proposed for environmental data reconstruction. The model combines two popular generative models, GAN and VAE, to generate features conditional on categorical data embedding after SWOT Analysis. The model is capable of generating features that resemble real feature distributions and adding sample factors to more accurately track individual sample data. Reconstructed data is… More >

Haizhen Wang^1,2,*, Jinying Yan^1,*, Na Jia¹

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 569-588, 2024, DOI:10.32604/cmc.2023.046055 - 30 January 2024

Abstract Encrypted traffic identification pertains to the precise acquisition and categorization of data from traffic datasets containing imbalanced and obscured content. The extraction of encrypted traffic attributes and their subsequent identification presents a formidable challenge. The existing models have predominantly relied on direct extraction of encrypted traffic data from imbalanced datasets, with the dataset’s imbalance significantly affecting the model’s performance. In the present study, a new model, referred to as UD-VLD (Unbalanced Dataset-VAE-LSTM-DRN), was proposed to address above problem. The proposed model is an encrypted traffic identification model for handling unbalanced datasets. The encoder of the… More >

Chih-Fang Huang^1,*, Cheng-Yuan Huang²

Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1939-1953, 2022, DOI:10.32604/iasc.2022.017559 - 09 December 2021

Abstract Musical emotion is important for the listener’s cognition. A smooth emotional expression generated through listening to music makes driving a car safer. Music has become more diverse and prolific with rapid technological developments. However, the cost of music production remains very high. At present, because the cost of music creation and the playing copyright are still very expensive, the music that needs to be listened to while driving can be executed by the way of automated composition of AI to achieve the purpose of driving safety and convenience. To address this problem, automated AI music… More >

R. Sujatha¹, B. Venkata Siva Krishna¹, Jyotir Moy Chatterjee², P. Rahul Naidu¹, NZ Jhanjhi^3,*, Challa Charita¹, Eza Nerin Mariya¹, Mohammed Baz⁴

Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 525-541, 2022, DOI:10.32604/iasc.2022.021216 - 26 October 2021

Abstract In the current times, COVID-19 has taken a handful of people’s lives. So, vaccination is crucial for everyone to avoid the spread of the disease. However, not every vaccine will be perfect or will get success for everyone. In the present work, we have analyzed the data from the Vaccine Adverse Event Reporting System and understood that the vaccines given to the people might or might not work considering certain demographic factors like age, gender, and multiple other variables like the state of living, etc. This variable is considered because it explains the unmentioned variables… More >