An Anomaly Detection Model for Network Intrusions Using One-Class SVM and Scaling Strategy. - Ming Zhang 021; Boyi Xu; Dongxia Wang (CollaborateCom, 2015)

Detecting network intrusion using Probabilistic Neural Network. - Ming Zhang 021; Junpeng Guo; Boyi Xu; Jie Gong (ICNC, 2015)

An Anomaly Detection Model Based on One-Class SVM to Detect Network Intrusions. - Ming Zhang 021; Boyi Xu; Jie Gong (MSN, 2015)

针对SSL/TLS的典型攻击 (Review of Typical Attacks on SSL/TLS). - Ming Zhang 021; Boyi Xu; Yanlai Guo (计算机科学, 2015)

A Deep Learning Method to Detect Web Attacks Using a Specially Designed CNN. - Ming Zhang 021; Boyi Xu; Shuai Bai; Shuaibing Lu; Zhechao Lin (ICONIP (5), 2017)

An Anomaly Detection Method Based on Multi-models to Detect Web Attacks. - Ming Zhang 021; Shuaibing Lu; Boyi Xu (ISCID (2), 2017)

Security Analysis of Intelligent Transportation Systems Based on Simulation Data. - Liang Ming; Gang Zhao; Minhuan Huang; Xiaohui Kuang; Hu Li; Ming Zhang 021 (ICDIS, 2018)

DeepWAF: Detecting Web Attacks Based on CNN and LSTM Models. - Xiaohui Kuang; Ming Zhang 021; Hu Li; Gang Zhao; Huayang Cao; Zhendong Wu; Xianmin Wang (CSS (2), 2019)

Kernel Vulnerability Analysis: A Survey. - Shuaibing Lu; Zhechao Lin; Ming Zhang 021 (DSC, 2019)

Neuron Selecting: Defending Against Adversarial Examples in Deep Neural Networks. - Ming Zhang 021; Hu Li; Xiaohui Kuang; Ling Pang; Zhendong Wu (ICICS, 2019)

Non-norm-bounded Attack for Generating Adversarial Examples. - Ming Zhang 021; Hu Li; Xiaohui Kuang; Yuanping Nie; Cheng Qian; Zhendong Wu; Gang Zhao (ICONIP (5), 2020)

A Study on Mesh Hybrid Memory Cube Network. - Cheng Qian; Ming Zhang 021; Xiaohui Kuang; Gang Zhao (ISPASS, 2020)

A Data-free Black-box Attack for Generating Transferable Adversarial Examples. - Chenxu Wang; Ming Zhang 021; Jinjing Zhao; Xiaohui Kuang; Han Zhang; Xuhong Zhang 002 (HPCC/DSS/SmartCity/DependSys, 2022)

Black-Box Adversarial Attacks on Deep Neural Networks: A Survey. - Chenxu Wang; Ming Zhang 021; Jinjing Zhao; Xiaohui Kuang (ICDIS, 2022)

Improving Transferability of Adversarial Examples with Virtual Step and Auxiliary Gradients. - Ming Zhang 021; Xiaohui Kuang; Hu Li; Zhendong Wu; Yuanping Nie; Gang Zhao (IJCAI, 2022)

Dynamic and Diverse Transformations for Defending Against Adversarial Examples. - Yongkang Chen; Ming Zhang 021; Jin Li; Xiaohui Kuang; Xuhong Zhang 002; Han Zhang (TrustCom, 2022)

Fooling Examples: Another Intriguing Property of Neural Networks. - Ming Zhang 021; Yongkang Chen; Cheng Qian (Sensors, 2023)

Dynamic loss yielding more transferable targeted adversarial examples. - Ming Zhang 021; Yongkang Chen; Hu Li; Cheng Qian; Xiaohui Kuang (Neurocomputing, 2024)