| Title | Dynamic malicious code detection technology based on deep learning |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Wei, Lizhuo, Xu, Fengkai, Zhang, Ni, Yan, Wei, Chai, Chuchu |
| Conference Name | 2022 20th International Conference on Optical Communications and Networks (ICOCN) |
| Keywords | Analytical models, API sequences, Deep Learning, dynamic analysis, human factors, malicious code, Malware, Metrics, optical fiber communication, policy-based governance, process control, pubcrawl, Resiliency, Safe Coding |
| Abstract | In this paper, the malicious code is run in the sandbox in a safe and controllable environment, the API sequence is deduplicated by the idea of the longest common subsequence, and the CNN and Bi-LSTM are integrated to process and analyze the API sequence. Compared with the method, the method using deep learning can have higher accuracy and work efficiency. |
| DOI | 10.1109/ICOCN55511.2022.9901158 |
| Citation Key | wei_dynamic_2022 |
Dynamic malicious code detection technology based on deep learning