Binu Sudhakaran Pillai¹, Raghavendra Kulkarni², Venkata Satya Suresh kumar Kondeti², Surendran Rajendran^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 1141-1166, 2025, DOI:10.32604/cmes.2025.070348 - 30 October 2025

Abstract Future 6G communications will open up opportunities for innovative applications, including Cyber-Physical Systems, edge computing, supporting Industry 5.0, and digital agriculture. While automation is creating efficiencies, it can also create new cyber threats, such as vulnerabilities in trust and malicious node injection. Denial-of-Service (DoS) attacks can stop many forms of operations by overwhelming networks and systems with data noise. Current anomaly detection methods require extensive software changes and only detect static threats. Data collection is important for being accurate, but it is often a slow, tedious, and sometimes inefficient process. This paper proposes a new… 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 >

Saad Alahmari^1,*, Abdulwhab Alkharashi²

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 849-873, 2025, DOI:10.32604/cmes.2025.062549 - 11 April 2025

Abstract The Internet of Things (IoT) is emerging as an innovative phenomenon concerned with the development of numerous vital applications. With the development of IoT devices, huge amounts of information, including users’ private data, are generated. IoT systems face major security and data privacy challenges owing to their integral features such as scalability, resource constraints, and heterogeneity. These challenges are intensified by the fact that IoT technology frequently gathers and conveys complex data, creating an attractive opportunity for cyberattacks. To address these challenges, artificial intelligence (AI) techniques, such as machine learning (ML) and deep learning (DL),… More >

Haizhen Wang^1,2,*, Na Jia^1,2, Yang He¹, Pan Tan^1,2

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3825-3851, 2024, DOI:10.32604/cmc.2024.055497 - 19 December 2024

Abstract As the popularity and dependence on the Internet increase, DDoS (distributed denial of service) attacks seriously threaten network security. By accurately distinguishing between different types of DDoS attacks, targeted defense strategies can be formulated, significantly improving network protection efficiency. DDoS attacks usually manifest as an abnormal increase in network traffic, and their diverse types of attacks, along with a severe data imbalance, make it difficult for traditional classification methods to effectively identify a small number of attack types. To solve this problem, this paper proposes a DDoS recognition method CVWGG (Conditional Variational Autoencoder-Wasserstein Generative Adversarial… More >

Lelisa Adeba Jilcha¹, Deuk-Hun Kim², Julian Jang-Jaccard³, Jin Kwak^4,*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3261-3284, 2023, DOI:10.32604/csse.2023.037615 - 03 April 2023

Abstract Contemporary attackers, mainly motivated by financial gain, consistently devise sophisticated penetration techniques to access important information or data. The growing use of Internet of Things (IoT) technology in the contemporary convergence environment to connect to corporate networks and cloud-based applications only worsens this situation, as it facilitates multiple new attack vectors to emerge effortlessly. As such, existing intrusion detection systems suffer from performance degradation mainly because of insufficient considerations and poorly modeled detection systems. To address this problem, we designed a blended threat detection approach, considering the possible impact and dimensionality of new attack surfaces… More >

Younghoon Ban, Jeong Hyun Yi, Haehyun Cho^*

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2215-2230, 2023, DOI:10.32604/csse.2023.036555 - 09 February 2023

Abstract Android malware has evolved in various forms such as adware that continuously exposes advertisements, banking malware designed to access users’ online banking accounts, and Short Message Service (SMS) malware that uses a Command & Control (C&C) server to send malicious SMS, intercept SMS, and steal data. By using many malicious strategies, the number of malware is steadily increasing. Increasing Android malware threats numerous users, and thus, it is necessary to detect malware quickly and accurately. Each malware has distinguishable characteristics based on its actions. Therefore, security researchers have tried to categorize malware based on their… More >

Jinfu Wang¹, Shunyi Zhao^1,*, Fei Liu¹, Zhenyi Ma²

CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.2, pp. 531-552, 2022, DOI:10.32604/cmes.2022.019521 - 15 June 2022

Abstract In modern industry, process monitoring plays a significant role in improving the quality of process conduct. With the higher dimensional of the industrial data, the monitoring methods based on the latent variables have been widely applied in order to decrease the wasting of the industrial database. Nevertheless, these latent variables do not usually follow the Gaussian distribution and thus perform unsuitable when applying some statistics indices, especially the T2 on them. Variational AutoEncoders (VAE), an unsupervised deep learning algorithm using the hierarchy study method, has the ability to make the latent variables follow the Gaussian More >

Zhipin Gu¹, Liangzhong He², Peiyan Li¹, Peng Sun³, Jiangyong Shi¹, Yuexiang Yang^1,*

Computer Systems Science and Engineering, Vol.39, No.3, pp. 307-320, 2021, DOI:10.32604/csse.2021.017969 - 12 August 2021

Abstract Federated learning is an ideal solution to the limitation of not preserving the users’ privacy information in edge computing. In federated learning, the cloud aggregates local model updates from the devices to generate a global model. To protect devices’ privacy, the cloud is designed to have no visibility into how these updates are generated, making detecting and defending malicious model updates a challenging task. Unlike existing works that struggle to tolerate adversarial attacks, the paper manages to exclude malicious updates from the global model’s aggregation. This paper focuses on Byzantine attack and backdoor attack in… More >