| Title | Malware Detection for Industrial Internet Based on GAN |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Li, Mingxuan, Lv, Shichao, Shi, Zhiqiang |
| Conference Name | 2020 IEEE International Conference on Information Technology,Big Data and Artificial Intelligence (ICIBA) |
| Keywords | adversarial sample, Detectors, Gallium nitride, Generative Adversarial Nets, generative adversarial networks, Generators, graph theory, Human Behavior, Industrial Internet, Internet, Malware, malware analysis, malware detection, Metrics, privacy, pubcrawl, resilience, Resiliency, Training |
| Abstract | This thesis focuses on the detection of malware in industrial Internet. The basic flow of the detection of malware contains feature extraction and sample identification. API graph can effectively represent the behavior information of malware. However, due to the high algorithm complexity of solving the problem of subgraph isomorphism, the efficiency of analysis based on graph structure feature is low. Due to the different scales of API graph of different malicious codes, the API graph needs to be normalized. Considering the difficulties of sample collection and manual marking, it is necessary to expand the number of malware samples in industrial Internet. This paper proposes a method that combines PageRank with TF-IDF to process the API graph. Besides, this paper proposes a method to construct the adversarial samples of malwares based on GAN. |
| DOI | 10.1109/ICIBA50161.2020.9276834 |
| Citation Key | li_malware_2020 |
Malware Detection for Industrial Internet Based on GAN