A high-performance deep learning architecture for host-based intrusion detection system

Tsern Huei Lee, Hsiao Yen Huang, Cheng Juang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publication2020 IEEE Region 10 Conference, TENCON 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1198-1202
Number of pages5
ISBN (Electronic)9781728184555
DOIs
StatePublished - 16 Nov 2020
Event2020 IEEE Region 10 Conference, TENCON 2020 - Virtual, Osaka, Japan
Duration: 16 Nov 202019 Nov 2020

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
Volume2020-November
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450

Conference

Conference2020 IEEE Region 10 Conference, TENCON 2020
CountryJapan
CityVirtual, Osaka
Period16/11/2019/11/20

Keywords

  • Autoencoder
  • Behavior Anomaly
  • Deep Learning
  • HIDS

Fingerprint Dive into the research topics of 'A high-performance deep learning architecture for host-based intrusion detection system'. Together they form a unique fingerprint.

Cite this