@Article{cmc.2025.064471,
AUTHOR = {Xuan Wu, Yafei Song, Xiaodan Wang, Peng Wang, Qian Xiang},
TITLE = {FSMMTD: A Feature Subset-Based Malicious Traffic Detection Method},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {84},
YEAR = {2025},
NUMBER = {1},
PAGES = {1279--1305},
URL = {http://www.techscience.com/cmc/v84n1/61765},
ISSN = {1546-2226},
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 (RNNs) and transformers, enabling superior processing of complex patterns and global information. This integration allows the FSMMTD model to enhance its capability in identifying intricate data relationships and capturing comprehensive contextual insights. The FSMMTD model monitors IoT data flows in real-time, efficiently detecting anomalies and enabling rapid response to potential intrusions. We validate our approach using the publicly available ToN_IoT dataset for IoT traffic analysis. Experimental results demonstrate that our method achieves superior performance with an accuracy of 98.37%, precision of 96.28%, recall of 95.36%, and F1-score of 96.79%. These metrics indicate that the FSMMTD model outperforms existing methods in detecting malicious traffic, showcasing its effectiveness and reliability in enhancing IoT network security.},
DOI = {10.32604/cmc.2025.064471}
}