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 >

Yuqi Chen^1,2, Guangjun Liang^1,2,3,*, Qun Wang^1,2,3

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1145-1167, 2025, DOI:10.32604/cmc.2025.063319 - 09 June 2025

Abstract Structured Query Language (SQL) injection attacks have become the most common means of attacking Web applications due to their simple implementation and high degree of harm. Traditional injection attack detection techniques struggle to accurately identify various types of SQL injection attacks. This paper presents an enhanced SQL injection detection method that utilizes content matching technology to improve the accuracy and efficiency of detection. Features are extracted through content matching, effectively avoiding the loss of valid information, and an improved deep learning model is employed to enhance the detection effect of SQL injections. Considering that grammar More >

Seo Yeon Moon¹, Byung Hyun Jo¹, Abir El Azzaoui¹, Sushil Kumar Singh², Jong Hyuk Park^1,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 25-55, 2025, DOI:10.32604/cmc.2025.062966 - 09 June 2025

Abstract With the rapid advancement of ICT and IoT technologies, the integration of Edge and Fog Computing has become essential to meet the increasing demands for real-time data processing and network efficiency. However, these technologies face critical security challenges, exacerbated by the emergence of quantum computing, which threatens traditional encryption methods. The rise in cyber-attacks targeting IoT and Edge/Fog networks underscores the need for robust, quantum-resistant security solutions. To address these challenges, researchers are focusing on Quantum Key Distribution and Post-Quantum Cryptography, which utilize quantum-resistant algorithms and the principles of quantum mechanics to ensure data confidentiality More >

Uddagiri Sirisha^1,*, Chanumolu Kiran Kumar², Sujatha Canavoy Narahari³, Parvathaneni Naga Srinivasu^4,5,6

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1757-1810, 2025, DOI:10.32604/cmc.2024.059715 - 17 February 2025

Abstract The growing spectrum of Generative Adversarial Network (GAN) applications in medical imaging, cyber security, data augmentation, and the field of remote sensing tasks necessitate a sharp spike in the criticality of review of Generative Adversarial Networks. Earlier reviews that targeted reviewing certain architecture of the GAN or emphasizing a specific application-oriented area have done so in a narrow spirit and lacked the systematic comparative analysis of the models’ performance metrics. Numerous reviews do not apply standardized frameworks, showing gaps in the efficiency evaluation of GANs, training stability, and suitability for specific tasks. In this work,… More >