Leveraging Transfer Learning for Enhanced Internet Security: Methods and Applications - Chen, Chang (Transactions on Computer Science and Intelligent Systems Research, 2024)
Internet technology is developing rapidly today. It not only brings new experiences to people but also brings huge security risks. The attack methods of cyber criminals are becoming more and more complex and unpredictable. The rise of artificial intelligence offers promising solutions for detecting and mitigating network attacks, but challenges such as insufficient and imbalanced datasets continue to hinder the effectiveness of these models. Transfer learning has emerged as a valuable technique to address these challenges by transferring knowledge and parameters from a source domain model to a target domain. This approach can reduce training costs, save time, enhance data utilization, and address data imbalance issues. This article provides a detailed examination of transfer learning, including its definition, methodologies, and specific applications within the Internet domain. It explores three key scenarios: network anomaly detection, malicious domain name detection, and rumor detection. By offering a comprehensive overview of transfer learning and its practical implementations, this paper hopes to help readers acquire a clear and comprehensive concept of transfer learning and its relevance to Internet security, serving as a valuable resource for those interested in this field.