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CNN-based anomaly detection for packet payloads of industrial control systemoa mark
  • Song, Joo Yeop ;
  • Paul, Rajib ;
  • Yun, Jeong Han ;
  • Kim, Hyoung Chun ;
  • Choi, Young June
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Publication Year
2021-01-01
Journal
International Journal of Sensor Networks
Publisher
Inderscience Publishers
Citation
International Journal of Sensor Networks, Vol.36 No.1, pp.36-49
Keyword
Anomaly detectionConvolutional neural networkIndustrial control systemIntrusion detectionn-gram methodnetwork securitySequence detectionSingle packet detection
Mesh Keyword
Anomaly detectionConvolutional neural networkIndustrial control systemsIntrusion Detection SystemsIntrusion-DetectionN-gram methodsNetworks securityPacket detectionSequence detectionSingle packet detection
All Science Classification Codes (ASJC)
Control and Systems EngineeringComputer Science ApplicationsComputer Networks and CommunicationsElectrical and Electronic Engineering
Abstract
Industrial control systems (ICSs) are more vulnerable to cyber threats owing to their network connectivity. The intrusion detection system(IDS) has been deployed to detect sophisticated cyber-Attack but the existing IDS uses the packet header information for traffic flow detection. IDS is inefficient to detect packet deformation; therefore, we propose the adoption of packet payload in IDS to respond to a variety of attacks and high performance. Our proposed model detects packet modification and traffic flowby inspecting each packet and sequence of packets. For evaluation, cross verification is conducted to increase the reliability of the statistics.
ISSN
1748-1287
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/32084
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85107952914&origin=inward
DOI
https://doi.org/10.1504/ijsnet.2021.115440
Journal URL
http://www.inderscience.com/ijsnet
Type
Article
Funding
This research was supported by Korea Electric Power Corporation [Grant number: 18A-013].
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Choi, Youngjune최영준
Department of Software and Computer Engineering
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