Abstract
We consider the case where a knowledge base consists of interactions among parameter values in an input parameter model for web application security testing. The input model gives rise to attack strings to be used for exploiting XSS vulnerabilities, a critical threat towards the security of web applications. Testing results are then annotated with a vulnerability triggering or non-triggering classification, and such security knowledge findings are added back to the knowledge base, making the resulting attack capabilities superior for newly requested input models. We present our approach as an iterative process that evolves an input model for security testing. Empirical evaluation on six real-world web application shows that the process effectively evolves a knowledge base for XSS vulnerability detection, achieving on average 78.8% accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
PhantomJS (http://phantomjs.org/) is a headless browser environment enabling introspection of events such as network requests, document edits and JavaScript errors.
- 2.
In theory, any valid JavaScript functions that will not be called during the normal operation of the SUT can be chosen instead.
- 3.
Note that \(t_{BEN}\) is different from the strength t for generating the initial test suite.
- 4.
In this case, we believe that the suspiciousness average and standard deviation could be useful indicators of the \(F_1\) score that the currently inferred model may have.
- 5.
WAVSEP: Web Application Vulnerability Scanner Evaluation Project, https://github.com/sectooladdict/wavsep.
- 6.
The tests suites were generated using the IpoF algorithm, implemented in ACTS.
References
Arcaini, P., Gargantini, A., Radavelli, M.: Efficient and guaranteed detection of t-way failure-inducing combinations. In: IEEE International Conference on Software Testing, Verification and Validation ICST Workshops (2019)
Bozic, J., Garn, B., Kapsalis, I., Simos, D., Winkler, S., Wotawa, F.: Attack pattern-based combinatorial testing with constraints for web security testing. In: IEEE International Conference on Software Quality, Reliability and Security (2015)
Catteddu, D.: Cloud computing: benefits, risks and recommendations for information security. In: Serrão, C., Aguilera DÃaz, V., Cerullo, F. (eds.) IBWAS 2009. CCIS, vol. 72, pp. 17–17. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-16120-9_9
Duchene, F., Groz, R., Rawat, S., Richier, J.-L.: XSS vulnerability detection using model inference assisted evolutionary fuzzing. In IEEE International Conference on Software Testing, Verification and Validation (ICST), pp. 815–817 (2012)
O. Foundation: OWASP Top 10 2017. https://www.owasp.org/index.php/Top_10-2017_A7-Cross-Site_Scripting_(XSS). Accessed 19 Apr 2018
Gargantini, A., Petke, J., Radavelli, M.: Combinatorial interaction testing for automated constraint repair. In: IEEE International Confernce on Software Testing, Verification and Validation ICST Workshops, pp. 239–248 (2017)
Gargantini, A., Radavelli, M.: Migrating combinatorial interaction test modeling and generation to the web. In: IEEE International Conference on Software Testing, Verification and Validation ICST Workshops, pp. 308–317 (2018)
Garn, B., Kapsalis, I., Simos, D.E., Winkler, S.: On the applicability of combinatorial testing to web application security testing: a case study. In: Proceedings of the 2014 Workshop on Joining AcadeMiA and Industry Contributions to Test Automation and Model-Based Testing, pp. 16–21. ACM (2014)
Ghandehari, L.S., Lei, Y., Kacker, R., Kuhn, D.R.R., Kung, D., Xie, T.: A combinatorial testing-based approach to fault localization. IEEE Trans. Softw. Eng. (2018)
Jia, Y., Cohen, M.B., Harman, M., Petke, J.: Learning combinatorial interaction test generation strategies using hyperheuristic search. In: Proceedings of the International Conference on Software Engineering - Volume 1, ICSE 2015, pp. 540–550 (2015)
Kuhn, D., Kacker, R., Lei, Y.: Introduction to Combinatorial Testing. Chapman & Hall/CRC, London (2013)
Melicher, W., Das, A., Sharif, M., Bauer, L., Jia, L.: Riding out DOMsday: towards detecting and preventing DOM cross-site scripting. In: Proceedings of Network and Distributed System Security Symposium. Internet Society (2018)
Simos, D.E., Kleine, K., Ghandehari, L.S.G., Garn, B., Lei, Y.: A combinatorial approach to analyzing cross-site scripting (XSS) vulnerabilities in web application security testing. In: Wotawa, F., Nica, M., Kushik, N. (eds.) ICTSS 2016. LNCS, vol. 9976, pp. 70–85. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47443-4_5
Simos, D.E., Kuhn, R., Voyiatzis, A.G., Kacker, R.: Combinatorial methods in security testing. IEEE Comput. 49, 40–43 (2016)
Temple, P., Galindo, J.A., Acher, M., Jézéquel, J.-M.: Using machine learning to infer constraints for product lines. In: Proceedings of the 20th International Systems and Software Product Line Conference, pp. 209–218. ACM (2016)
Yu, L., Lei, Y., Kacker, R.N., Kuhn, D.R.: ACTS: a combinatorial test generation tool. In: IEEE International Conference on Software Testing, Verification and Validation (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Garn, B., Radavelli, M., Gargantini, A., Leithner, M., Simos, D.E. (2019). A Fault-Driven Combinatorial Process for Model Evolution in XSS Vulnerability Detection. In: Wotawa, F., Friedrich, G., Pill, I., Koitz-Hristov, R., Ali, M. (eds) Advances and Trends in Artificial Intelligence. From Theory to Practice. IEA/AIE 2019. Lecture Notes in Computer Science(), vol 11606. Springer, Cham. https://doi.org/10.1007/978-3-030-22999-3_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-22999-3_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-22998-6
Online ISBN: 978-3-030-22999-3
eBook Packages: Computer ScienceComputer Science (R0)