Suhyeon Lee¹, Hwankuk Kim^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2657-2682, 2025, DOI:10.32604/cmes.2025.070627 - 26 November 2025

Abstract The open nature and heterogeneous architecture of Open Radio Access Network (Open RAN) undermine the consistency of security policies and broaden the attack surface, thereby increasing the risk of security vulnerabilities. The dynamic nature of network performance and traffic patterns in Open RAN necessitates advanced detection models that can overcome the constraints of traditional techniques and adapt to evolving behaviors. This study presents a methodology for effectively detecting malicious traffic in Open RAN by utilizing an Artificial-Intelligence/Machine-Learning (AI/ML) Framework. A hybrid Transformer–Convolutional-Neural-Network (Transformer-CNN) ensemble model is employed for anomaly detection. The proposed model generates final 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 >

So-Eun Jeon¹, Ye-Sol Oh¹, Yeon-Ji Lee¹, Il-Gu Lee^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1669-1687, 2024, DOI:10.32604/cmes.2024.047239 - 20 May 2024

Abstract With the advancement of wireless network technology, vast amounts of traffic have been generated, and malicious traffic attacks that threaten the network environment are becoming increasingly sophisticated. While signature-based detection methods, static analysis, and dynamic analysis techniques have been previously explored for malicious traffic detection, they have limitations in identifying diversified malware traffic patterns. Recent research has been focused on the application of machine learning to detect these patterns. However, applying machine learning to lightweight devices like IoT devices is challenging because of the high computational demands and complexity involved in the learning process. In… More >

R. D. Pubudu L. Indrasiri¹, Ernesto Lee², Vaibhav Rupapara³, Furqan Rustam⁴, Imran Ashraf^5,*

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 489-515, 2022, DOI:10.32604/cmc.2022.019636 - 03 November 2021

Abstract Malicious traffic detection over the internet is one of the challenging areas for researchers to protect network infrastructures from any malicious activity. Several shortcomings of a network system can be leveraged by an attacker to get unauthorized access through malicious traffic. Safeguard from such attacks requires an efficient automatic system that can detect malicious traffic timely and avoid system damage. Currently, many automated systems can detect malicious activity, however, the efficacy and accuracy need further improvement to detect malicious traffic from multi-domain systems. The present study focuses on the detection of malicious traffic with high… More >