Optimal Timing of Moving Target Defense: A Stackelberg Game Model

As an effective approach to thwarting advanced attacks, moving target defense (MTD) has been applied to various domains. Previous works on MTD, however, mainly focus on deciding the sequence of system configurations to be used and have largely ignored the equally important timing problem. Given that both the migration cost and attack time vary over system configurations, it is crucial to jointly optimize the spatial and temporal decisions in MTD to better protect the system from persistent threats. In this work, we propose a Stackelberg game model for MTD where the defender commits to a joint migration and timing strategy to cope with configuration-dependent migration cost and attack time distribution. The defender's problem is formulated as a semi-Markovian decision process and a nearly optimal MTD strategy is derived by exploiting the unique structure of the game.

[1]  Michael Franz,et al.  Runtime Defense against Code Injection Attacks Using Replicated Execution , 2011, IEEE Transactions on Dependable and Secure Computing.

[2]  Ness B. Shroff,et al.  When to Reset Your Keys: Optimal Timing of Security Updates via Learning , 2017, AAAI.

[3]  Sailik Sengupta,et al.  A Game Theoretic Approach to Strategy Generation for Moving Target Defense in Web Applications , 2017, AAMAS.

[4]  Ronald L. Rivest,et al.  FlipIt: The Game of “Stealthy Takeover” , 2012, Journal of Cryptology.

[5]  Aron Laszka,et al.  Mitigating Covert Compromises - A Game-Theoretic Model of Targeted and Non-Targeted Covert Attacks , 2013, WINE.

[6]  Milind Tambe,et al.  Security and Game Theory - Algorithms, Deployed Systems, Lessons Learned , 2011 .

[7]  Scott A. DeLoach,et al.  A model for analyzing the effect of moving target defenses on enterprise networks , 2014, CISR '14.

[8]  Jack W. Davidson,et al.  Security through redundant data diversity , 2008, 2008 IEEE International Conference on Dependable Systems and Networks With FTCS and DCC (DSN).

[9]  Dawn Xiaodong Song,et al.  SoK: Eternal War in Memory , 2013, 2013 IEEE Symposium on Security and Privacy.

[10]  Rob Sloan,et al.  Advanced Persistent Threat , 2014 .

[11]  Ehab Al-Shaer,et al.  Openflow random host mutation: transparent moving target defense using software defined networking , 2012, HotSDN '12.

[12]  Quanyan Zhu,et al.  Game-Theoretic Approach to Feedback-Driven Multi-stage Moving Target Defense , 2013, GameSec.

[13]  Ming Zhang,et al.  A Game Theoretic Model for Defending Against Stealthy Attacks with Limited Resources , 2015, GameSec.

[14]  Rolando Cavazos-Cadena,et al.  Value iteration and approximately optimal stationary policies in finite-state average Markov decision chains , 2002, Math. Methods Oper. Res..

[15]  Prasant Mohapatra,et al.  A Stackelberg Game and Markov Modeling of Moving Target Defense , 2017, GameSec.

[16]  P. Schweitzer.  Perturbation theory and finite Markov chains , 1968 .

[17]  Ehab Al-Shaer,et al.  Spatio-temporal Address Mutation for Proactive Cyber Agility against Sophisticated Attackers , 2014, MTD '14.

[18]  Claire Le Goues,et al.  GenProg: A Generic Method for Automatic Software Repair , 2012, IEEE Transactions on Software Engineering.

[19]  Azer Bestavros,et al.  Markov Modeling of Moving Target Defense Games , 2016, MTD@CCS.

[20]  Quanyan Zhu,et al.  A Game-theoretic Taxonomy and Survey of Defensive Deception for Cybersecurity and Privacy , 2017, ACM Comput. Surv..

[21]  Haifeng Xu,et al.  Deceiving Cyber Adversaries: A Game Theoretic Approach , 2018, AAMAS.