Abstract
Vulnerability severity assessment is an important research problem. Common Vulnerability Scoring System (CVSS) has been widely used to quantitatively assess the vulnerability severity, but its assessment process relies on human experts to determine metric values, which makes the assessment process tedious and subjective. This calls for tools that can assess the vulnerability severity automatically and objectively. In this paper, we move a step forward in this direction by proposing an approach for automatic assessment of vulnerability severity based on attack process, dubbed 
(AutoCVSS). The key insight is to leverage characteristics and rules we define to model the CVSS base metrics, and assess the vulnerability severity more automatically and objectively by capturing the attributes related to the characteristics during the attack process. In order to evaluate AutoCVSS, we reproduce the attacks for 98 vulnerabilities from Linux kernel, FTP service, and Apache service with their exploits. The experimental results show that the vulnerability severity scores automatically obtained by AutoCVSS are basically in accordance with those assessed manually by security experts in the National Vulnerability Database (NVD), which verifies the effectiveness of our approach.
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References
Common Vulnerability Scoring System. https://www.first.org/cvss/
Exploit database. https://www.exploit-db.com/
National Vulnerability Database. https://nvd.nist.gov/
Allodi, L., Banescu, S., Femmer, H., Beckers, K.: Identifying relevant information cues for vulnerability assessment using CVSS. In: Proceedings of the 8th ACM Conference on Data and Application Security and Privacy (CODASPY), pp. 119–126. ACM (2018)
Allodi, L., Biagioni, S., Crispo, B., Labunets, K., Massacci, F., Santos, W.: Estimating the assessment difficulty of CVSS environmental metrics: an experiment. In: Dang, T.K., Wagner, R., Küng, J., Thoai, N., Takizawa, M., Neuhold, E.J. (eds.) FDSE 2017. LNCS, vol. 10646, pp. 23–39. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70004-5_2
Almasizadeh, J., Azgomi, M.A.: A stochastic model of attack process for the evaluation of security metrics. Comput. Netw. 57(10), 2159–2180 (2013)
Cheng, P., Wang, L., Jajodia, S., Singhal, A.: Aggregating CVSS base scores for semantics-rich network security metrics. In: Proceedings of the 31st Symposium on Reliable Distributed Systems (SRDS), pp. 31–40. IEEE (2012)
Del Valle, S., Hethcote, H., Hyman, J.M., Castillo-Chavez, C.: Effects of behavioral changes in a smallpox attack model. Math. Biosci. 195(2), 228–251 (2005)
Gallon, L.: On the impact of environmental metrics on CVSS scores. In: Proceedings of the 2nd International Conference on Social Computing (SocialCom), pp. 987–992. IEEE (2010)
Ghani, H., Luna, J., Khelil, A., Alkadri, N., Suri, N.: Predictive vulnerability scoring in the context of insufficient information availability. In: Proceedings of 2013 International Conference on Risks and Security of Internet and Systems (CRiSIS), pp. 1–8. IEEE (2013)
Hu, H., Zhang, H., Liu, Y., Wang, Y.: Quantitative method for network security situation based on attack prediction. Secur. Commun. Netw. 2017, 1–19 (2017)
Huang, H., Zhao, F., Ye, M.: Estimate the influential level of vulnerability instance based on hybrid ranking for dynamic network attacking scenarios. In: Proceedings of the 10th International Conference on Information Sciences Signal Processing and their Applications (ISSPA), pp. 586–589. IEEE (2010)
Khazaei, A., Ghasemzadeh, M., Derhami, V.: An automatic method for CVSS score prediction using vulnerabilities description. J. Intell. Fuzzy Syst. 30(1), 89–96 (2016)
Luk, C., et al.: Pin: building customized program analysis tools with dynamic instrumentation. In: Proceedings of Conference on Programming Language Design and Implementation, pp. 190–200. ACM (2005)
Luo, J., Lo, K., Qu, H.: A software vulnerability rating approach based on the vulnerability database. J. Appl. Math. 2014, 932397:1–932397:9 (2014)
Ross, D.M., Wollaber, A.B., Trepagnier, P.C.: Latent feature vulnerability ranking of CVSS vectors. In: Proceedings of the Summer Simulation Multi-Conference, pp. 19:1–19:12. Society for Computer Simulation International (2017)
Spanos, G., Sioziou, A., Angelis, L.: WIVSS: a new methodology for scoring information systems vulnerabilities. In: Proceedings of the 17th Panhellenic Conference on Informatics, pp. 83–90. ACM (2013)
Tripathi, A., Singh, U.K.: Estimating risk levels for vulnerability categories using CVSS. Int. J. Internet Technol. Secured Trans. 4(4), 272–289 (2012)
Younis, A.A., Malaiya, Y.K.: Comparing and evaluating CVSS base metrics and Microsoft rating system. In: Proceedings of the IEEE International Conference on Software Quality, Reliability and Security (QRS), pp. 252–261. IEEE (2015)
Younis, A.A., Malaiya, Y.K., Ray, I.: Using attack surface entry points and reachability analysis to assess the risk of software vulnerability exploitability. In: Proceedings of the 15th International Symposium on High-Assurance Systems Engineering (HASE), pp. 1–8. IEEE (2014)
Younis, A.A., Malaiya, Y.K., Ray, I.: Assessing vulnerability exploitability risk using software properties. Software Qual. J. 24(1), 159–202 (2016)
Younis, A., Malaiya, Y.K., Ray, I.: Evaluating CVSS base score using vulnerability rewards programs. In: Hoepman, J.-H., Katzenbeisser, S. (eds.) SEC 2016. IAICT, vol. 471, pp. 62–75. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-33630-5_5
Acknowledgments
This paper is supported by the National Key Research & Development (R&D) Plan of China under grant No. 2017YFB0802205, the National Science Foundation of China under grant No. 61672249, and the Shenzhen Fundamental Research Program under grant No. JCYJ20170413114215614.
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Zou, D., Yang, J., Li, Z., Jin, H., Ma, X. (2019). AutoCVSS: An Approach for Automatic Assessment of Vulnerability Severity Based on Attack Process. In: Miani, R., Camargos, L., Zarpelão, B., Rosas, E., Pasquini, R. (eds) Green, Pervasive, and Cloud Computing. GPC 2019. Lecture Notes in Computer Science(), vol 11484. Springer, Cham. https://doi.org/10.1007/978-3-030-19223-5_17
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