1e416013186c20a17cb4d5a 20230720015405783 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Innovative Technology and Exploring Engineering IJITEE 22783075 10.35940/ijitee https://www.ijitee.org/ 07 30 2023 12 8 Posts and Telecommunications Institute of Technology Nguyen Duc Viet https://orcid.org/0009-0009-5609-2905 Dang Dinh Quan https://orcid.org/0009-0009-0531-5523 Faculty of Information Technology, Hanoi University. The attack technique by the malware distribution form is a dangerous, difficult to detect and prevent attack method. Current malware detection studies and proposals are often based on two main methods: using sign sets and analyzing abnormal behaviors using machine learning or deep learning techniques. This paper will propose a method to detect malware on Endpoints based on Event IDs using deep learning. Event IDs are behaviors of malware tracked and collected on Endpoints' operating system kernel. The malware detection proposal based on Event IDs is a new research approach that has not been studied and proposed much. To achieve this purpose, this paper proposes to combine different data mining methods and deep learning algorithms. The data mining process is presented in detail in section 2 of the paper. 07 30 2023 21 27 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true International Journal of Innovative Technology and Exploring Engineering (IJITEE) No, we did not receive. No conflicts of interest to the best of our knowledge. No, the article does not require ethical approval and consent to participate with evidence. Not relevant. All authors have equal participation in this article. 10.35940/ijitee.H9651.0712823 https://www.ijitee.org/portfolio-item/H96510712823/