Tong Wu, Dawei Zhou, Qingyu Ou^*, Fang Luo

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073482 - 12 January 2026

Abstract The increasing interconnection of modern industrial control systems (ICSs) with the Internet has enhanced operational efficiency, but also made these systems more vulnerable to cyberattacks. This heightened exposure has driven a growing need for robust ICS security measures. Among the key defences, intrusion detection technology is critical in identifying threats to ICS networks. This paper provides an overview of the distinctive characteristics of ICS network security, highlighting standard attack methods. It then examines various intrusion detection methods, including those based on misuse detection, anomaly detection, machine learning, and specialised requirements. This paper concludes by exploring More >

Hanaa Nafea¹, Awais Qasim², Sana Abdul Sattar², Adeel Munawar³, Muhammad Nadeem Ali⁴, Byung-Seo Kim^4,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072281 - 12 January 2026

Abstract The increased connectivity and reliance on digital technologies have exposed smart transportation systems to various cyber threats, making intrusion detection a critical aspect of ensuring their secure operation. Traditional intrusion detection systems have limitations in terms of centralized architecture, lack of transparency, and vulnerability to single points of failure. This is where the integration of blockchain technology with signature-based intrusion detection can provide a robust and decentralized solution for securing smart transportation systems. This study tackles the issue of database manipulation attacks in smart transportation networks by proposing a signature-based intrusion detection system. The introduced More >

Haohui Su¹, Xuan Zhang^1,*, Lvjun Zheng¹, Xiaojie Shen², Hua Liao¹

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072093 - 12 January 2026

Abstract Distributed Denial-of-Service (DDoS) attacks pose severe threats to Industrial Control Networks (ICNs), where service disruption can cause significant economic losses and operational risks. Existing signature-based methods are ineffective against novel attacks, and traditional machine learning models struggle to capture the complex temporal dependencies and dynamic traffic patterns inherent in ICN environments. To address these challenges, this study proposes a deep feature-driven hybrid framework that integrates Transformer, BiLSTM, and KNN to achieve accurate and robust DDoS detection. The Transformer component extracts global temporal dependencies from network traffic flows, while BiLSTM captures fine-grained sequential dynamics. The learned… More >

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 >

Adeel Munawar¹, Muhammad Nadeem Ali², Awais Qasim³, Byung-Seo Kim^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 1189-1211, 2025, DOI:10.32604/cmes.2025.071876 - 30 October 2025

Abstract Cyber-physical systems (CPS) represent a sophisticated integration of computational and physical components that power critical applications such as smart manufacturing, healthcare, and autonomous infrastructure. However, their extensive reliance on internet connectivity makes them increasingly susceptible to cyber threats, potentially leading to operational failures and data breaches. Furthermore, CPS faces significant threats related to unauthorized access, improper management, and tampering of the content it generates. In this paper, we propose an intrusion detection system (IDS) optimized for CPS environments using a hybrid approach by combining a nature-inspired feature selection scheme, such as Grey Wolf Optimization (GWO),… More >

Lincong Zhao¹, Liandong Chen¹, Peipei Shen¹, Zizhou Liu¹, Chengzhu Li¹, Fanqin Zhou^2,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5057-5071, 2025, DOI:10.32604/cmc.2025.067521 - 23 October 2025

Abstract The rapid growth of Internet of things devices and the emergence of rapidly evolving network threats have made traditional security assessment methods inadequate. Federated learning offers a promising solution to expedite the training of security assessment models. However, ensuring the trustworthiness and robustness of federated learning under multi-party collaboration scenarios remains a challenge. To address these issues, this study proposes a shard aggregation network structure and a malicious node detection mechanism, along with improvements to the federated learning training process. First, we extract the data features of the participants by using spectral clustering methods combined… More >

Fatma S. Alrayes¹, Syed Umar Amin^2,*, Nada Ali Hakami², Mohammed K. Alzaylaee³, Tariq Kashmeery⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 581-614, 2025, DOI:10.32604/cmes.2025.065188 - 31 July 2025

Abstract The growing incidence of cyberattacks necessitates a robust and effective Intrusion Detection Systems (IDS) for enhanced network security. While conventional IDSs can be unsuitable for detecting different and emerging attacks, there is a demand for better techniques to improve detection reliability. This study introduces a new method, the Deep Adaptive Multi-Layer Attention Network (DAMLAN), to boost the result of intrusion detection on network data. Due to its multi-scale attention mechanisms and graph features, DAMLAN aims to address both known and unknown intrusions. The real-world NSL-KDD dataset, a popular choice among IDS researchers, is used to… More >

Sunil K. Singh¹, Sudhakar Kumar^1,*, Manraj Singh¹, Savita Gupta², Razaz Waheeb Attar³, Varsha Arya^4,5, Ahmed Alhomoud⁶, Brij B. Gupta^7,8,9

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3927-3941, 2025, DOI:10.32604/cmc.2025.059643 - 03 July 2025

Abstract As quantum computing continues to advance, traditional cryptographic methods are increasingly challenged, particularly when it comes to securing critical systems like Supervisory Control and Data Acquisition (SCADA) systems. These systems are essential for monitoring and controlling industrial operations, making their security paramount. A key threat arises from Shor’s algorithm, a powerful quantum computing tool that can compromise current hash functions, leading to significant concerns about data integrity and confidentiality. To tackle these issues, this article introduces a novel Quantum-Resistant Hash Algorithm (QRHA) known as the Modular Hash Learning Algorithm (MHLA). This algorithm is meticulously crafted… More >

Sunghyun Yu, Yoojae Won^*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3943-3980, 2025, DOI:10.32604/cmes.2025.064997 - 30 June 2025

Abstract Pre-Authentication and Post-Connection (PAPC) plays a crucial role in realizing the Zero Trust security model by ensuring that access to network resources is granted only after successful authentication. While earlier approaches such as Port Knocking (PK) and Single Packet Authorization (SPA) introduced pre-authentication concepts, they suffer from limitations including plaintext communication, protocol dependency, reliance on dedicated clients, and inefficiency under modern network conditions. These constraints hinder their applicability in emerging distributed and resource-constrained environments such as AIoT and browser-based systems. To address these challenges, this study proposes a novel port-sequence-based PAPC scheme structured as a… More >

Xuan Wu¹, Yafei Song¹, Xiaodan Wang^1,*, Peng Wang¹, Qian Xiang²

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1279-1305, 2025, DOI:10.32604/cmc.2025.064471 - 09 June 2025

Abstract With the growth of the Internet of Things (IoT) comes a flood of malicious traffic in the IoT, intensifying the challenges of network security. Traditional models operate with independent layers, limiting their effectiveness in addressing these challenges. To address this issue, we propose a cross-layer cooperative Feature Subset-Based Malicious Traffic Detection (FSMMTD) model for detecting malicious traffic. Our approach begins by applying an enhanced random forest method to adaptively filter and retain highly discriminative first-layer features. These processed features are then input into an improved state-space model that integrates the strengths of recurrent neural networks… More >