ADAPT: A Game Inspired Attack-Defense and Performance Metric Taxonomy - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2013

ADAPT: A Game Inspired Attack-Defense and Performance Metric Taxonomy

Chris B. Simmons
  • Function : Author
  • PersonId : 1001111
Sajjan G. Shiva
  • Function : Author
  • PersonId : 1001112
Harkeerat Singh Bedi
  • Function : Author
  • PersonId : 1001113
Vivek Shandilya
  • Function : Author
  • PersonId : 1001114


Game theory has been researched extensively in network security demonstrating an advantage of modeling the interactions between attackers and defenders. Game theoretic defense solutions have continuously evolved in most recent years. One of the pressing issues in composing a game theoretic defense system is the development of consistent quantifiable metrics to select the best game theoretic defense model. We survey existing game theoretic defense, information assurance, and risk assessment frameworks that provide metrics for information and network security and performance assessment. Coupling these frameworks, we propose a game theoretic approach to attack-defense and performance metric taxonomy (ADAPT). ADAPT uses three classifications of metrics: (i) Attacker, (ii) Defender (iii) Performance. We proffer ADAPT with an attempt to aid game theoretic performance metrics. We further propose a game decision system (GDS) that uses ADAPT to compare competing game models. We demonstrate our approach using a distributed denial of service (DDoS) attack scenario.
Fichier principal
Vignette du fichier
978-3-642-39218-4_26_Chapter.pdf (377.22 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01463837 , version 1 (09-02-2017)





Chris B. Simmons, Sajjan G. Shiva, Harkeerat Singh Bedi, Vivek Shandilya. ADAPT: A Game Inspired Attack-Defense and Performance Metric Taxonomy. 28th Security and Privacy Protection in Information Processing Systems (SEC), Jul 2013, Auckland, New Zealand. pp.344-365, ⟨10.1007/978-3-642-39218-4_26⟩. ⟨hal-01463837⟩
63 View
167 Download



Gmail Facebook Twitter LinkedIn More