A Honeypot-based Attack Detection Method for Networked Inverted Pendulum System

Baoyue Xu, Dajun Du, Changda Zhang, Jin Zhang

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


The data transmitted via the network may be vulnerable to cyber attacks in networked inverted pendulum system (NIPS), how to detect cyber attacks is a challenging issue. To solve this problem, this paper investigates a honeypot-based attack detection method for NIPS. Firstly, honeypot for NIPS attack detection (namely NipsPot) is constructed by deceptive environment module of a virtual closed-loop control system, and the stealthiness of typical covert attacks is analysed. Secondly, attack data is collected by NipsPot, which is used to train supported vector machine (SVM) model for attack detection. Finally, simulation results demonstrate that NipsPot-based attack detector can achieve the accuracy rate of 99.78%, the precision rate of 98.75%, and the recall rate of 100%.

Original languageEnglish
Title of host publicationProceedings of the 40th Chinese Control Conference, CCC 2021
EditorsChen Peng, Jian Sun
PublisherIEEE Computer Society
Number of pages6
ISBN (Electronic)9789881563804
StatePublished - 26 Jul 2021
Externally publishedYes
Event40th Chinese Control Conference, CCC 2021 - Shanghai, China
Duration: 26 Jul 202128 Jul 2021

Publication series

NameChinese Control Conference, CCC
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927


Conference40th Chinese Control Conference, CCC 2021


  • NipsPot
  • covert attacks
  • networked inverted pendulum system
  • supported vector machine


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