A Lightweight Malware Traffic Classification Method Based on a Broad Learning Architecture
Yibin Zhang, Guan Gui, Shiwen Mao
Abstract
Malware traffic classification (MTC) plays an important role for securing the Internet of Things (IoT). Many machine learning (ML) and deep learning (DL)-based MTC methods have been proposed in recent years. However, the former still requires human intervention, while the latter incurs considerable computation overheads. To address these problems, we propose a broad learning (BL)-aided MTC method (BL-MTC), which is a lightweight and graphics processing unit-free solution with good performance and extremely low cost. The simulation results show that the proposed BL-MTC method not only achieves superior results on the USTC-TFC2016 data set but also exhibits an exponential advantage in computation overhead.
Topics & Concepts
Computer scienceGraphics processing unitOverhead (engineering)MalwareComputationArtificial intelligenceGraphicsDeep learningMachine learningOperating systemAlgorithmNetwork Security and Intrusion DetectionInternet Traffic Analysis and Secure E-votingMachine Learning and ELM