Quantifying the Security of Composed Systems

  • Max Walter
  • Carsten Trinitis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3911)


The authors recommend to quantify the security of a complex system by first quantifying the security of its components, and, in a second step, by calculating the overall security according to a given method. This paper summarizes the state of the art of security measures for components and presents a new method for combining these measures into the system’s security. The proposed method starts with an intuitive graphical representation of the system. This representation is converted into an algebraic expression using abstract AND, OR, and MEAN operators. Applying application-dependent semantics to these operators will allow for an evaluation of the model.


Compose System Successful Attack Information Assurance Computer Network Security Unbounded Random Variable 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Schuedel, G., Wood, B.: Adversary Work Factor as a Metric for Information Assurance. In: Proc. of the New Security Paradigm Workshop (2000)Google Scholar
  2. 2.
    Schneier, B.: Attack trees. Dr. Dobb’s Journal (1999)Google Scholar
  3. 3.
    Schneier, B.: Secrets and Lies – Digital Security in a Networked World. Wiley and Sons, Chichester (2000)Google Scholar
  4. 4.
    Jonsson, E., Olovsson, T.: A quantitative model of the security intrusion process based on attacker behavior. IEEE Transactions on Software Engineering 23, 235–245 (1997)CrossRefGoogle Scholar
  5. 5.
    Wang, C., Wulf, W.: Towards a Framework for Security Measurement. In: Proc. of the National Information Systems Security Conference (NISSC 1997) (1997)Google Scholar
  6. 6.
    Kotenko, I., Mankov, E.: Experiments with Simulation of Attacks against Computer Networks. In: Gorodetsky, V., Popyack, L.J., Skormin, V.A. (eds.) MMM-ACNS 2003. LNCS, vol. 2776, pp. 187–198. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  7. 7.
    Nicol, D.M., Sanders, W.H., Trivedi, K.S.: From Dependability to Security. IEEE Transactions on Dependable and Secure Computing 1, 48–65 (2004)CrossRefGoogle Scholar
  8. 8.
    Tidwell, T., Larson, R., Fitch, K., Hale, J.: Modeling internet attacks. In: Proc. of the 2001 IEEE Workshop on Information Assurance and Security, pp. 54–59 (2001)Google Scholar
  9. 9.
    Steffan, J., Schumacher, M.: Collaborative attack modeling. In: Proc. of the 17th ACM Symposium on Applied Computing (SAC 2002), pp. 253–259 (2002)Google Scholar
  10. 10.
    Hunstad, A., Hallberg, J.: Design for securability - Applying engineering principles to the design of security architectures. In: Proc. of the Workshop of Application of Engineering Principles to System Security Design (WAEPSSD) (2002)Google Scholar
  11. 11.
    Voas, J., Ghosh, A., McGraw, G., Charron, F.: Defining an Adaptive Software Security Metric from a Dynamic Software Failure Tolerance Measure. In: Proc. of the 11th Annual Conference on Computer Assurance (COMPASS 1996) (1996)Google Scholar
  12. 12.
    Levi, D.: Lessons learned in using live red teams in IA experiments. In: Proc. of the DARPA Information Survivability Conference and Exposition, vol. 1, pp. 110–119. IEEE, Los Alamitos (2003)CrossRefGoogle Scholar
  13. 13.
    Vaughn, R.B., Henning, R., Sira, A.: Information Assurance Measures and Metrics - State of Practice and Proposed Taxonomy. In: Proc. of the 36th Annual Hawaii International Conference on System Sciences (HICSS 2003), vol. 9 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Max Walter
    • 1
  • Carsten Trinitis
    • 1
  1. 1.Lehrstuhl für Rechnertechnik und Rechnerorganisation (LRR)Technische Universität MünchenMünchenGermany

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