Using Knowledge Graph and Deep Reinforcement Learning for Malware Detection
Hsiao-Chung Lin, Yi-Wen Liao, Ping Wang, Wen‐Hui Lin, Yu-Hsiang Lin · 2024
With the development of e-commerce, e-commerce security incidents have caused information security to attract the attention of the general public. According to statistics, a large number of computers are infected by malware such as Trojans and zombies, and are used by the botnet. Malware can invade computer systems and exploit system or network vulnerabilities for intrusion. For malware detection, machine learning algorithms are used to detect malware, but they cannot incorporate background knowledge. Malware detection needs to be integrated with knowledge graphs, deep learning, and reinforcement learning. In this study, the proposed model was developed by employing deep learning, graph neural network, knowledge graph, and reinforcement learning to detect malware. To evaluate the effectiveness of the proposed model, the proposed model was evaluated with malware samples collected from MalwareBazaar, DikeDataset, and VirusShare and examined with accuracy, precision, and recall. Experimental results showed that the proposed model detected malware effectively.