Affiliations: [a] Faculty of Information Technology, Posts and Telecommunications Institute of Technology, Hanoi, Vietnam
| [b] FPT University, Hanoi, Vietnam
Correspondence:
[*]
Corresponding author. Cho Do Xuan, Faculty of Information Technology, Posts and Telecommunications Institute of Technology, Hanoi, Vietnam. E-mail: [email protected].
Abstract: The Advanced Persistent Threat (APT) attack is a form of dangerous, intentionally and clearly targeted attack. Currently, the APT attack trend is through the end-users and then escalating privileges in the system by spreading malware which is widely used by attackers. Therefore, the problem of early detection and warning of the APT attack malware on workstations is urgent. In this paper, we propose a new approach to APT malware detection on workstations based on the technique of analyzing and evaluating process profiles. The characteristics and principles of our proposed method are as follows: Firstly, processes are collected and aggregated into process profiles of APT malware; Secondly, these process profiles are used by Graph2Vec graph analysis algorithm to extract the characteristics of the process profile. Finally, in order to conclude about the sign of malicious APT, this paper proposes to use Long short-term memory (LSTM) and bidirectional LSTM (BiLSTM) algorithm. With the proposed approach in the paper, we have not only succeeded in building and synthesizing APT malware behavior on Workstations as a basis to improve the efficiency of predicting APT malware, but also have opened up a new approach to the task of synthesizing and analyzing anomalous behavior of malware.
Keywords: APT, APT malware detection on Workstation, Event ID, deeplearning, process profile, Graph2Vec
