Malicious Powershell Detection Using Graph Convolution Network

Autor: Sunoh Choi
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Applied Sciences, Vol 11, Iss 14, p 6429 (2021)
Druh dokumentu: article
ISSN: 2076-3417
DOI: 10.3390/app11146429
Popis: The internet’s rapid growth has resulted in an increase in the number of malicious files. Recently, powershell scripts and Windows portable executable (PE) files have been used in malicious behaviors. To solve these problems, artificial intelligence (AI) based malware detection methods have been widely studied. Among AI techniques, the graph convolution network (GCN) was recently introduced. Here, we propose a malicious powershell detection method using a GCN. To use the GCN, we needed an adjacency matrix. Therefore, we proposed an adjacency matrix generation method using the Jaccard similarity. In addition, we show that the malicious powershell detection rate is increased by approximately 8.2% using GCN.
Databáze: Directory of Open Access Journals