Visible to the public Biblio

Filters: Author is Jiang, Zhengwei  [Clear All Filters]
Conference Paper
Zhao, Zhijun, Jiang, Zhengwei, Wang, Yueqiang, Chen, Guoen, Li, Bo.  2019.  Experimental Verification of Security Measures in Industrial Environments. 2019 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC). :498–502.
Industrial Control Security (ICS) plays an important role in protecting Industrial assets and processed from being tampered by attackers. Recent years witness the fast development of ICS technology. However there are still shortage of techniques and measures to verify the effectiveness of ICS approaches. In this paper, we propose a verification framework named vICS, for security measures in industrial environments. vICS does not requires installing any agent in industrial environments, and could be viewed as a non-intrusive way. We use vICS to evaluate the effectiveness of classic ICS techniques and measures through several experiments. The results shown that vICS provide an feasible solution for verifying the effectiveness of classic ICS techniques and measures for industrial environments.
Wang, Shuwei, Wang, Qiuyun, Jiang, Zhengwei, Wang, Xuren, Jing, Rongqi.  2021.  A Weak Coupling of Semi-Supervised Learning with Generative Adversarial Networks for Malware Classification. 2020 25th International Conference on Pattern Recognition (ICPR). :3775–3782.
Malware classification helps to understand its purpose and is also an important part of attack detection. And it is also an important part of discovering attacks. Due to continuous innovation and development of artificial intelligence, it is a trend to combine deep learning with malware classification. In this paper, we propose an improved malware image rescaling algorithm (IMIR) based on local mean algorithm. Its main goal of IMIR is to reduce the loss of information from samples during the process of converting binary files to image files. Therefore, we construct a neural network structure based on VGG model, which is suitable for image classification. In the real world, a mass of malware family labels are inaccurate or lacking. To deal with this situation, we propose a novel method to train the deep neural network by Semi-supervised Generative Adversarial Network (SGAN), which only needs a small amount of malware that have accurate labels about families. By integrating SGAN with weak coupling, we can retain the weak links of supervised part and unsupervised part of SGAN. It improves the accuracy of malware classification by making classifiers more independent of discriminators. The results of experimental demonstrate that our model achieves exhibiting favorable performance. The recalls of each family in our data set are all higher than 93.75%.