Haoyu Wu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Haoyu Wu has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Cheng Xiong
College of Artificial Intelligence, Tianjin University of Science and Technology, Tianjin 300457, China
1 shared publication - Cong Wang
Tianjin University of Science and Technology
1 shared publication - Maode Ma
Faculty of Computer Science and Artificial Intelligence, Shenzhen University of Advanced Technology, Shenzhen, China
1 shared publication - Yabao Hu
College of Artificial Intelligence, Tianjin University of Science and Technology, Tianjin 300457, China
1 shared publication - Yulong Gan
College of Artificial Intelligence, Tianjin University of Science and Technology, Tianjin 300457, China
1 shared publication
Latest Publications
- Open Access
GDM-DTM: A Group Decision-Making-Enabled Dynamic Trust Management Method for Malicious Node Detection in Low-Altitude UAV Networks
As a core enabler of the emerging low-altitude economy, UAV networks face significant security risks during operation, including malicious node infiltration and data tampering. Existing trust management schemes suffer from deficiencies such as strong reliance on infrastructure, insufficient capability for multi-dimensional trust evaluation, and vulnerability to collusion attacks. To address these issues, this paper proposes a group decision-making (GDM)-enabled dynamic trust management method, termed GDM-DTM, for low-altitude UAV networks. GDM-DTM comprises four core parts: Subjective Consistency Evaluation, Objective Consistency Evaluation, Global Consistency Evaluation, and Self-Proof Consistency Evaluation. Furthermore, the method integrates a Dynamic Trust Adjustment Mechanism with multi-attribute trust computation, enabling efficient trust evaluation independent of ground infrastructure and thereby facilitating effective malicious UAV detection. The experimental results demonstrate that under identical conditions with a malicious node ratio of 30%, GDM-DTM achieves an accuracy of 85.04% and an F-score of 91.66%. Compared to the current state-of-the-art methods, this represents an improvement of 6.04 percentage points in accuracy and 3.71 percentage points in F-score.
Open AccessGDM-DTM: A Group Decision-Making-Enabled Dynamic Trust Management Method for Malicious Node Detection in Low-Altitude UAV Networks
As a core enabler of the emerging low-altitude economy, UAV networks face significant security risks during operation, including malicious node infiltration and data tampering. Existing trust management schemes suffer from deficiencies such as strong reliance on infrastructure, insufficient capability for multi-dimensional trust evaluation, and vulnerability to collusion attacks. To address these issues, this paper proposes a group decision-making (GDM)-enabled dynamic trust management method, termed GDM-DTM, for low-altitude UAV networks. GDM-DTM comprises four core parts: Subjective Consistency Evaluation, Objective Consistency Evaluation, Global Consistency Evaluation, and Self-Proof Consistency Evaluation. Furthermore, the method integrates a Dynamic Trust Adjustment Mechanism with multi-attribute trust computation, enabling efficient trust evaluation independent of ground infrastructure and thereby facilitating effective malicious UAV detection. The experimental results demonstrate that under identical conditions with a malicious node ratio of 30%, GDM-DTM achieves an accuracy of 85.04% and an F-score of 91.66%. Compared to the current state-of-the-art methods, this represents an improvement of 6.04 percentage points in accuracy and 3.71 percentage points in F-score.