Skip to main content

Cyber Camouflage Games for Strategic Deception

Part of the Lecture Notes in Computer Science book series (LNSC,volume 11836)


The rapid increase in cybercrime, causing a reported annual economic loss of $600 billion (Lewis 2018), has prompted a critical need for effective cyber defense. Strategic criminals conduct network reconnaissance prior to executing attacks to avoid detection and establish situational awareness via scanning and fingerprinting tools. Cyber deception attempts to foil these reconnaissance efforts by camouflaging network and system attributes to disguise valuable information. Game-theoretic models can identify decisions about strategically deceiving attackers, subject to domain constraints. For effectively deploying an optimal deceptive strategy, modeling the objectives and the abilities of the attackers, is a key challenge. To address this challenge, we present Cyber Camouflage Games (CCG), a general-sum game model that captures attackers which can be diversely equipped and motivated. We show that computing the optimal defender strategy is NP-hard even in the special case of unconstrained CCGs, and present an efficient approximate solution for it. We further provide an MILP formulation accelerated with cut-augmentation for the general constrained problem. Finally, we provide experimental evidence that our solution methods are efficient and effective.


  • Game theory
  • Cyber deception
  • Optimization

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions


  1. 1.

    The additional constant M can be simply replaced by \(\max _{i, i'} |v^\mathrm{a}_i- v^\mathrm{a}_{i'}|\) and \(\max _{i, i'} |v^\mathrm{d}_i- v^\mathrm{d}_{i'}|\) resp. in the 3rd, 4th constraints.


Download references


This research was sponsored by the Army Research Office (grant W911NF-17-1-0370) and also in part by National Science Foundation (grant IIS-1850477) and Army Reserch Lab’s Cyber Security CRA (grant W911NF-13-2-00).

Author information

Authors and Affiliations


Corresponding author

Correspondence to Omkar Thakoor .

Editor information

Editors and Affiliations



Complete MILP formulation for OP (2)

We let \(\underline{v}^\mathrm{d}\), \(\overline{v}^\mathrm{d}\) denote the least and the highest defender valuations, and similarly, \(\underline{v}^\mathrm{a}\), \(\overline{v}^\mathrm{a}\) the least and the highest attacker valuations. To linearize, we let the variables \(X_{kj}\), \(Y_{kj}\), and \(Z_{kj}\) represent the bilinear terms \((1 - q_j)\varTheta _{kj}\), \(\alpha \varTheta _{kj}\), and \(\gamma \varTheta _{kj}\) respectively and add liner constraints which enforce the appropriate product value to them. The resultant MILP is as follows.

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Thakoor, O., Tambe, M., Vayanos, P., Xu, H., Kiekintveld, C., Fang, F. (2019). Cyber Camouflage Games for Strategic Deception. In: Alpcan, T., Vorobeychik, Y., Baras, J., Dán, G. (eds) Decision and Game Theory for Security. GameSec 2019. Lecture Notes in Computer Science(), vol 11836. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-32429-2

  • Online ISBN: 978-3-030-32430-8

  • eBook Packages: Computer ScienceComputer Science (R0)