Software Behavior Model Measuring Approach of Combining Structural Analysis and Language Set
Structural analysis represented by FSMDiff algorithm is the main measuring approach for existing software behavior model which is based on finite state automata. This method just focus on the data structure of finite state automata as figure characteristics, however, as software behavior model, it is more important for finite state automaton to reflect the characteristics of software behavior. So we need to find out a method to distinguish the importance in the finite state automata between different state nodes. This paper shows how the output of the FSMDiff algorithm can provide a quantified expression of structural difference between two models. According to this, we also introduce the language-set analysis, which uses the depth-first traversal algorithm to solve the language set of finite state automata. Above all, we propose a new strategy of assigning weights for the local elements of software behavior model, which can fusion assigning weight results and structural analysis for evaluation of software behavioral models. Experiment results demonstrate the effectiveness and feasibility of software behavioral model measuring approach of combining structural analysis and language set, and laid the foundation for constructing evaluation system of software behavior model inference technology.
KeywordsSoftware behavior model Finite state automata Structural analysis Language-set FSMDiff algorithm
This work was supported by the Key Project of National Defense Basic Research Program of China (Grant No. B1120132031) and the Ph.D. Programs Foundation of Ministry of Education of China (Grant No. 20131101120043).
- 1.Wang, X.Z., Sun, L.C., Lu, Y.L.: Intrusion detection approach towards software behavior trustworthiness. J. Univ. Sci. Technol. China 41(7), 626–635 (2011)Google Scholar
- 2.Peng, G.J., Tao, F., Zhang, H.G.: Research on theory model of software dynamic trustiness based on behavior integrity. In: Proceedings of the 2009 International Conference on Multimedia Information Networking and Security, pp. 130–134. Washington, DC, USA (2009)Google Scholar
- 3.Godefroid, E., Levin, M.Y., Molnar, D.: Automated whitebox fuzz testing. In: Proceedings of the 16th Annual Network & Distributed System Security Symposium, pp. 1–10. San Diego, USA (2008)Google Scholar
- 5.Quante, J., Koschke, R.: Dynamic protocol recovery. In: Proceedings of the 14th International Working Conference on Reverse Engineering, pp. 219–228. Vancouver, Canada (2007)Google Scholar
- 6.Hopcroft, J., Motwani, R., Ullman, J.: Introduction to automata theory, languages and computation, 3rd edn. Addison-Wesley, New Jersey (2007)Google Scholar
- 7.Walkinshaw, N., Bogdanov, K.: Comparing software behavior models. Technical report: CS-08-16, The University of Sheffield, Sheffield, UK (2008)Google Scholar
- 9.Lo, D., Khoo, S.: QUARK: empirical assessment of automaton-based specification miners. In: Proceedings of the 13th Working Conference on Reverse Engineering, pp. 51–60. Benevento, Italy (2006)Google Scholar