Feng Qin
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Feng Qin has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Jie Yang
University of Electronic Science and Technology of China
1 shared publication - Qianni Zhang
Queen Mary University of London, London, UK
1 shared publication - Xuyang Ding
University of Electronic Science and Technology of China
1 shared publication - Ying Xie
Southwest Minzu University
1 shared publication - Yongzhao Zhang
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
1 shared publication
Latest Publications
- 27 April 2026
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Internet of Things Journal
- Vol. 13 (14) , 31377-31390
An Intrusion Feature Selection Method Based on Feature Distribution and Gini Impurity
Intrusion detection systems (IDS) can effectively monitor network traffic and accurately detect malicious behaviors. In Internet of Things (IoT) environments, the massive influx of heterogeneous, resource-constrained devices introduces more complex security challenges, making IDS even more crucial for maintaining network security. However, the presence of redundant or irrelevant features in network traffic can significantly degrade the detection performance of IDS. To address this issue, this paper proposes a Feature distribution and Gini Impurity Filter-based intrusion feature selection method (FGIF). It combines the cardinality and Gini impurity distributions of features within a dataset to construct a multi-parameter evaluation framework, which is used to define efficient feature filtering rules that eliminate redundant and irrelevant features. Theoretical analysis demonstrates that, compared to entropy-based methods, FGIF mitigates selection bias during the feature selection process and significantly reduces computational overhead. Experiments conducted on six widely used IDS benchmark datasets and five commonly adopted classification models further confirm its effectiveness. FGIF significantly reduces feature dimensionality while maintaining detection performance comparable to that of the full feature set. Moreover, compared to existing state-of-the-art methods, FGIF achieves a superior balance between dimensionality reduction and model performance.
- 27 April 2026
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Internet of Things Journal
- Vol. 13 (14) , 31377-31390
An Intrusion Feature Selection Method Based on Feature Distribution and Gini Impurity
Intrusion detection systems (IDS) can effectively monitor network traffic and accurately detect malicious behaviors. In Internet of Things (IoT) environments, the massive influx of heterogeneous, resource-constrained devices introduces more complex security challenges, making IDS even more crucial for maintaining network security. However, the presence of redundant or irrelevant features in network traffic can significantly degrade the detection performance of IDS. To address this issue, this paper proposes a Feature distribution and Gini Impurity Filter-based intrusion feature selection method (FGIF). It combines the cardinality and Gini impurity distributions of features within a dataset to construct a multi-parameter evaluation framework, which is used to define efficient feature filtering rules that eliminate redundant and irrelevant features. Theoretical analysis demonstrates that, compared to entropy-based methods, FGIF mitigates selection bias during the feature selection process and significantly reduces computational overhead. Experiments conducted on six widely used IDS benchmark datasets and five commonly adopted classification models further confirm its effectiveness. FGIF significantly reduces feature dimensionality while maintaining detection performance comparable to that of the full feature set. Moreover, compared to existing state-of-the-art methods, FGIF achieves a superior balance between dimensionality reduction and model performance.