Optimal Denial-of-Service Attack Scheduling with Energy Constraint over Packet-Dropping Networks

Jiahu Qin*, Menglin Li, Ling Shi, Xinghuo Yu

*Corresponding author for this work

Research output: Contribution to journalJournal Articlepeer-review

327 Citations (Scopus)

Abstract

The recent years have seen a surge of security issues of cyber-physical systems (CPS). In this paper, denial-of-service (DoS) attack scheduling is investigated in depth. Specifically, we consider a system where a remote estimator receives the data packet sent by a sensor over a wireless network at each time instant, and an energy-constrained attacker that cannot launch DoS attacks all the time designs the optimal DoS attack scheduling to maximize the attacking effect on the remote estimation performance. Most of the existing works concerning DoS attacks focus on the ideal scenario in which data packets can be received successfully if there is no DoS attack. To capture the unreliability nature of practical networks, we study the packet-dropping network in which packet dropouts may occur even in the absence of attack. We derive the optimal attack scheduling scheme that maximizes the average expected estimation error, and the one which maximizes the expected terminal estimation error over packet-dropping networks. We also present some countermeasures against DoS attacks, and discuss the optimal defense strategy, and how the optimal attack schedule can serve for more effective and resource-saving countermeasures. We further investigate the optimal attack schedule with multiple sensors. The optimality of the theoretical results is demonstrated by numerical simulations.

Original languageEnglish
Pages (from-to)1648-1663
Number of pages16
JournalIEEE Transactions on Automatic Control
Volume63
Issue number6
DOIs
Publication statusPublished - Jun 2018

Bibliographical note

Publisher Copyright:
© 1963-2012 IEEE.

Keywords

  • Cyber-physical systems (CPS)
  • denial-of-service (DoS) attack
  • energy constraint
  • packet-dropping networks
  • remote state estimation

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