Tornadoes in the cloud: Worst-case attacks on distributed resources systems

Jhonatan Tavori, Hanoch Levy

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

Abstract

Geographically distributed cloud networks are used by a variety of applications and services worldwide. As the demand for these services increases, their data centers form an attractive target for malicious attackers, aiming at harming the services. In this study we address sophisticated attackers who aim at causing maximal-damage to the service.A worst-case (damage-maximizing) attack is an attack which minimizes the revenue of the system operator, due to disrupting the users from being served. A sophisticated attacker needs to decide how many attacking agents should be launched at each of the systems regions, in order to inflict maximal damage.We characterize and analyze damage-maximization strategies for a number of attacks including deterministic attack, concur-rent stochastic agents attack, approximation of a virus-spread attack and over-size binomial attack. We also address user-migration defense, allowing to dynamically migrate demands among regions, and we provide efficient algorithms for deriving worst-case attacks given a system with arbitrary placement and demands. The results form a basis for devising resource allocation strategies aiming at minimizing attack damages.

Original languageEnglish
Title of host publicationINFOCOM 2021 - IEEE Conference on Computer Communications
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9780738112817
DOIs
StatePublished - 10 May 2021
Event40th IEEE Conference on Computer Communications, INFOCOM 2021 - Vancouver, Canada
Duration: 10 May 202113 May 2021

Publication series

NameProceedings - IEEE INFOCOM
Volume2021-May
ISSN (Print)0743-166X

Conference

Conference40th IEEE Conference on Computer Communications, INFOCOM 2021
Country/TerritoryCanada
CityVancouver
Period10/05/2113/05/21

Keywords

  • Cyber attacks
  • Distributed networks
  • Resource allocation
  • Stochastic optimization

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