A lightweight model design approach for few-shot malicious traffic classification.

ABSTRACT: Classifying malicious traffic, which can trace the lineage of attackers' malicious families, is fundamental to safeguarding cybersecurity. However, the deep learning approaches currently employed require substantial volumes of data, conflicting with the challenges in acquiring and accurately labeling malicious traffic data. Additionally, edge network devices vulnerable to cyber-attacks often cannot meet the computational demands required to deploy deep learning models. The rapid mutation of malicious activities further underscores the need for models with strong generalization capabilities to adapt to evolving threats. This paper introduces an innovative few-shot malicious traffic classification method that is precise, lightweight, and exhibits enhanced generalization. By refining traditio

SUBMITTER: Wang R 

PROVIDER: S-EPMC11493955 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature