Games of Timing for Security in Dynamic Environments

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• 关键词：Security ; Game Theory ; Games of timing ; Vulnerability discovery
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9406
• 期：1
• 页码：57-73
• 全文大小：309 KB
• 参考文献：1.Blackwell, D.: The noisy duel, one bullet each, arbitrary accuracy. Technical report, The RAND Corporation, D-442 (1949)
  2.Böhme, R., Schwartz, G.: Modeling cyber-insurance: towards a unifying framework. In: 9th Workshop on the Economics of Information Security (WEIS) (2010)
  3. Bowers, K.D., van Dijk, M., Griffin, R., Juels, A., Oprea, A., Rivest, R.L., Triandopoulos, N.: Defending against the unknown enemy: applying FlipIt to system security. In: Grossklags, J., Walrand, J. (eds.) GameSec 2012. LNCS, vol. 7638, pp. 248–263. Springer, Heidelberg (2012) CrossRef
  4.Chen, P., Kataria, G., Krishnan, R.: Correlated failures, diversification, and information security risk management. MIS Q. 35(2), 397–422 (2011)
  5.Feng, X., Zheng, Z., Hu, P., Cansever, D., Mohapatra, P.: Stealthy attacks meets insider threats: a three-player game model. Technical report
  6. Fultz, N., Grossklags, J.: Blue versus red: towards a model of distributed security attacks. In: Dingledine, R., Golle, P. (eds.) FC 2009. LNCS, vol. 5628, pp. 167–183. Springer, Heidelberg (2009) CrossRef
  7.Gordon, L., Loeb, M.: The economics of information security investment. ACM Trans. Inf. Syst. Secur. 5(4), 438–457 (2002)CrossRef
  8.Grossklags, J., Christin, N., Chuang, J.: Secure or insure?: a game-theoretic analysis of information security games. In: Proceedings of the 17th International World Wide Web Conference, pp. 209–218 (2008)
  9.Grossklags, J., Reitter, D.: How task familiarity and cognitive predispositions impact behavior in a security game of timing. In: Proceedings of the 27th IEEE Computer Security Foundations Symposium (CSF), pp. 111–122 (2014)
  10.Hu, P., Li, H., Fu, H., Cansever, D., Mohapatra, P.: Dynamic defense strategy against advanced persistent threat with insiders. In: Proceedings of the 34th IEEE International Conference on Computer Communications (INFOCOM) (2015)
  11. Ioannidis, C., Pym, D., Williams, J.: Investments and trade-offs in the economics of information security. In: Dingledine, R., Golle, P. (eds.) FC 2009. LNCS, vol. 5628, pp. 148–166. Springer, Heidelberg (2009) CrossRef
  12. Johnson, B., Böhme, R., Grossklags, J.: Security games with market insurance. In: Baras, J.S., Katz, J., Altman, E. (eds.) GameSec 2011. LNCS, vol. 7037, pp. 117–130. Springer, Heidelberg (2011) CrossRef
  13.Johnson, B., Laszka, A., Grossklags, J.: The complexity of estimating systematic risk in networks. In: Proceedings of the 27th IEEE Computer Security Foundations Symposium (CSF), pp. 325–336 (2014)
  14.Kunreuther, H., Heal, G.: Interdependent security. J. Risk Uncertain. 26(2), 231–249 (2003)CrossRef MATH
  15.Laszka, A., Felegyhazi, M., Buttyan, L.: A survey of interdependent information security games. ACM Comput. Surv. 47(2), 23:1–23:38 (2014)CrossRef
  16. Laszka, A., Horvath, G., Felegyhazi, M., Buttyán, L.: FlipThem: modeling targeted attacks with flipit for multiple resources. In: Poovendran, R., Saad, W. (eds.) GameSec 2014. LNCS, vol. 8840, pp. 175–194. Springer, Heidelberg (2014)
  17. Laszka, A., Johnson, B., Grossklags, J.: Mitigating covert compromises. In: Chen, Y., Immorlica, N. (eds.) WINE 2013. LNCS, vol. 8289, pp. 319–332. Springer, Heidelberg (2013) CrossRef
  18. Laszka, A., Johnson, B., Grossklags, J.: Mitigation of targeted and non-targeted covert attacks as a timing game. In: Das, S.K., Nita-Rotaru, C., Kantarcioglu, M. (eds.) GameSec 2013. LNCS, vol. 8252, pp. 175–191. Springer, Heidelberg (2013) CrossRef
  19.Lelarge, M., Bolot, J.: Economic incentives to increase security in the internet: the case for insurance. In: Proceedings of the 33rd IEEE International Conference on Computer Communications (INFOCOM), pp. 1494–1502 (2009)
  20.Manshaei, M., Zhu, Q., Alpcan, T., Bacşar, T., Hubaux, J.: Game theory meets network security and privacy. ACM Comput. Surv. 45(3), 25:1–25:39 (2013)CrossRef
  21.Nochenson, A., Grossklags, J.: A behavioral investigation of the FlipIt game. In: 12th Workshop on the Economics of Information Security (WEIS) (2013)
  22.Ozment, A.: The likelihood of vulnerability rediscovery and the social utility of vulnerability hunting. In: Proceedings of the 4th Workshop on the Economics of Information Security (WEIS) (2005)
  23.Ozment, A., Schechter, S.: Milk or wine: does software security improve with age? In: Proceedings of the 15th USENIX Security Symposium (2006)
  24.Pal, R., Huang, X., Zhang, Y., Natarajan, S., Hui, P.: On security monitoring in sdns: a strategic outlook
  25. Panaousis, E., Fielder, A., Malacaria, P., Hankin, C., Smeraldi, F.: Cybersecurity games and investments: a decision support approach. In: Poovendran, R., Saad, W. (eds.) GameSec 2014. LNCS, vol. 8840, pp. 266–286. Springer, Heidelberg (2014)
  26. Pham, V., Cid, C.: Are we compromised? modelling security assessment games. In: Grossklags, J., Walrand, J. (eds.) GameSec 2012. LNCS, vol. 7638, pp. 234–247. Springer, Heidelberg (2012) CrossRef
  27.Radzik, T.: Results and problems in games of timing. In: Lecture Notes-Monograph Series. Statistics, Probability and Game Theory: Papers in Honor of David Blackwell, vol. 30, pp. 269–292 (1996)
  28.Reitter, D., Grossklags, J., Nochenson, A.: Risk-seeking in a continuous game of timing. In: Proceedings of the 13th International Conference on Cognitive Modeling (ICCM), pp. 397–403 (2013)
  29.Rescorla, E.: Is finding security holes a good idea? IEEE Secur. Priv. 3(1), 14–19 (2005)CrossRef
  30. Schechter, S.E., Smith, M.D.: How much security is enough to stop a thief? In: Wright, R.N. (ed.) FC 2003. LNCS, vol. 2742, pp. 122–137. Springer, Heidelberg (2003) CrossRef
  31.Van Dijk, M., Juels, A., Oprea, A., Rivest, R.: Flipit: the game of “stealthy takeover”. J. Crypt. 26(4), 655–713 (2013)CrossRef MATH
  32.Varian, H.: System reliability and free riding. In: Camp, J., Lewis, S. (eds.) Economics of Information Security, pp. 1–15. Kluwer Academic Publishers, Dordrecht (2004)CrossRef
  33. Wellman, M.P., Prakash, A.: Empirical game-theoretic analysis of an adaptive cyber-defense scenario (preliminary report). In: Poovendran, R., Saad, W. (eds.) GameSec 2014. LNCS, vol. 8840, pp. 43–58. Springer, Heidelberg (2014)
  34.Zhang, M., Zheng, Z., Shroff, N.: Stealthy attacks and observable defenses: a game theoretic model under strict resource constraints. In: Proceedings of the IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp. 813–817 (2014)
  35.Zhao, M., Grossklags, J., Chen, K.: An exploratory study of white hat behaviors in a web vulnerability disclosure program. In: Proceedings of the ACM Workshop on Security Information Workers, pp. 51–58 (2014)
  36.Zhao, M., Grossklags, J., Liu, P.: An empirical study of web vulnerability discovery ecosystems. In: Proceedings of the 22nd ACM Conference on Computer and Communications Security (CCS) (2015)
  
• 作者单位：Benjamin Johnson (16)
  Aron Laszka (17)
  Jens Grossklags (18)
  
  16. CyLab, Carnegie Mellon University, Pittsburgh, USA
  17. Institute for Software Integrated Systems, Vanderbilt University, Nashville, USA
  18. College of Information Sciences and Technology, Pennsylvania State University, University Park, USA
  
• 丛书名：Decision and Game Theory for Security
• ISBN：978-3-319-25594-1
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Increasing concern about insider threats, cyber-espionage, and other types of attacks which involve a high degree of stealthiness has renewed the desire to better understand the timing of actions to audit, clean, or otherwise mitigate such attacks. However, to the best of our knowledge, the modern literature on games shares a common limitation: the assumption that the cost and effectiveness of the players’ actions are time-independent. In practice, however, the cost and success probability of attacks typically vary with time, and adversaries may only attack when an opportunity is present (e.g., when a vulnerability has been discovered).