Network Traffic-Oriented Malware Detection in IoT

Attacks against IoT (Internet-of-Things) infrastructure, applications and end devices have increased significantly. Typical malware in IoT generates a high volume of malicious traffic. Thus, this paper improves the malware byte sequence-based MalConv model. A malicious traffic feature-based Bi-LSTM (Bidirectional Long Short-Term Memory) model is integrated. Finally, we design a fused malware detection model applicable for end devices in IoT. The experiment results demonstrate that the fused Network Traffic-based MalConv (NT-MalConv) achieves higher detection performance with 95.17% accuracy. NT-MalConv outperforms the improved MalConv and is 10.31% better in accuracy when detecting adversarial samples.