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CONCEAL: A Strategy Composition for Resilient Cyber Deception: Framework, Metrics, and Deployment

  • Ehab Al-Shaer
  • Jinpeng Wei
  • Kevin W. Hamlen
  • Cliff Wang
Chapter

Abstract

Cyber deception is a key proactive cyber resilience technique to reverse the current asymmetry that favors adversaries in cyber warfare by creating a significant confusion in discovering and targeting cyber assets. One of the key objectives for cyber deception is to hide the true identity of the cyber assets in order to effectively deflect adversaries away from critical targets, and detect their activities early in the kill chain.

Although many cyber deception techniques were proposed including using honeypots to represent fake targets and mutating IP addresses to frequently change the ground truth of the network configuration (Jafarian et al., IEEE Transactions on Information Forensics and Security 10(12):2562–2577 (2015)), none of these deception techniques is resilient enough to provide high confidence of concealing the identity of the network assets, particularly against sophisticated attackers. In fact, in this chapter our analytical and experimental work showed that highly resilient cyber deception is unlikely attainable using a single technique, but it requires an optimal composition of various concealment techniques to maximize the deception utility. We, therefore, present a new cyber deception framework, called CONCEAL, which is a composition of mutation, anonymity, and diversity to maximize key deception objectives, namely concealability, detectability, and deterrence, while constraining the overall deployment cost. We formally define the CONCEAL metrics for concealability, detectability, and deterrence to measure the effectiveness of CONCEAL. Finally, we present the deployment of CONCEAL as a service to achieve manageability and cost-effectiveness by automatically generating the optimal deception proxy configuration based on existing host/network configuration, risk constraints of network services, and budget constraints. Our evaluation experiments measure both the deception effectiveness based on the above metrics and the scalability of the CONCEAL framework.

Notes

Acknowledgements

This research was supported in part by United States Army Research Office under contract number W911NF1510361. Any opinions, findings, conclusions or recommendations stated in this material are those of the authors and do not necessarily reflect the views of the funding sources.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ehab Al-Shaer
    • 1
  • Jinpeng Wei
    • 2
  • Kevin W. Hamlen
    • 3
  • Cliff Wang
    • 4
  1. 1.Department of Software & Information SystemUniversity of North Carolina CharlotteCharlotteUSA
  2. 2.Department of Software and Information SystemUniversity of North CarolinaCharlotteUSA
  3. 3.Computer Science DepartmentUniversity of Texas at DallasRichardsonUSA
  4. 4.Computing and Information Science DivisionArmy Research OfficeDurhamUSA

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