MalDAE: Detecting and explaining malware based on correlation and fusion of static and dynamic characteristics. - Weijie Han; Jingfeng Xue; Yong Wang 010; Lu Huang 002; Zixiao Kong; Limin Mao (Comput. Secur., 2019)

Detection of Malicious Domains in APT via Mining Massive DNS Logs. - Lu Huang 002; Jingfeng Xue; Weijie Han; Zixiao Kong; Zequn Niu (ML4CS (1), 2020)

A Survey on Adversarial Attack in the Age of Artificial Intelligence. - Zixiao Kong; Jingfeng Xue; Yong Wang 010; Lu Huang 002; Zequn Niu; Feng Li (Wirel. Commun. Mob. Comput., 2021)

Privacy-Preserving and Traceable Federated Learning for data sharing in industrial IoT applications. - Junbao Chen; Jingfeng Xue; Yong Wang 010; Lu Huang 002; Thar Baker; Zhixiong Zhou (Expert Syst. Appl., 2023)

A novel anomaly detection approach based on ensemble semi-supervised active learning (ADESSA). - Zequn Niu; Wenjie Guo; Jingfeng Xue; Yong Wang 010; Zixiao Kong; Lu Huang 002 (Comput. Secur., 2023)

WHGDroid: Effective android malware detection based on weighted heterogeneous graph. - Lu Huang 002; Jingfeng Xue; Yong Wang 010; Zhenyan Liu; Junbao Chen; Zixiao Kong (J. Inf. Secur. Appl., 2023)

Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept Drift. - Junbao Chen; Jingfeng Xue; Yong Wang 010; Zhenyan Liu; Lu Huang 002 (NeurIPS, 2024)

Strengthening LLM ecosystem security: Preventing mobile malware from manipulating LLM-based applications. - Lu Huang 002; Jingfeng Xue; Yong Wang 010; Junbao Chen; Tianwei Lei (Inf. Sci., 2024)