A High-Performance Deep Learning Architecture for Host-based Intrusion Detection System
Tsern‐Huei Lee, Hsiao-Yen Huang, C. Juang · 2020
Host-based intrusion detection system (HIDS) is a necessary component for network security, especially when more and more data are encrypted which makes network-based intrusion detection system lose its functionality of packet content inspection. After many years of research, it is widely acknowledged that system calls are the preferred data source for HIDS. In a recent paper, a novel semantic analysis approach was proposed and shown to achieve the best performance, as compared with various previous syntactic analysis schemes. The performance difference is profound for modern attacks. However, the semantic analysis approach requires considerable computational complexity. In this paper, we present a deep learning architecture which requires no data pre-processing and is easy to train. Experimental results show that our design has a better performance than the semantic analysis approach.