US 11,892,897 B2
Systems and methods for predicting which software vulnerabilities will be exploited by malicious hackers to prioritize for patching
Paulo Shakarian, Tempe, AZ (US); Mohammed Almukaynizi, Tempe, AZ (US); Jana Shakarian, Tempe, AZ (US); Eric Nunes, Tempe, AZ (US); Krishna Dharaiya, Tempe, AZ (US); Manoj Balasubramaniam Senguttuvan, Tempe, AZ (US); and Alexander Grimm, Tempe, AZ (US)
Assigned to Arizona Board of Regents on Behalf of Arizona State University, Tempe, AZ (US)
Appl. No. 16/640,878
Filed by Arizona Board of Regents on Behalf of Arizona State University, Tempe, AZ (US)
PCT Filed Oct. 26, 2018, PCT No. PCT/US2018/057812
§ 371(c)(1), (2) Date Feb. 21, 2020,
PCT Pub. No. WO2019/089389, PCT Pub. Date May 9, 2019.
Claims priority of provisional application 62/581,123, filed on Nov. 3, 2017.
Prior Publication US 2020/0356675 A1, Nov. 12, 2020
Int. Cl. G06F 21/57 (2013.01); G06F 11/00 (2006.01); G06F 21/54 (2013.01); G06F 21/55 (2013.01); G06F 18/24 (2023.01); G06F 18/214 (2023.01)
CPC G06F 11/008 (2013.01) [G06F 18/2148 (2023.01); G06F 18/24 (2023.01); G06F 21/54 (2013.01); G06F 21/552 (2013.01); G06F 21/577 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method for assessing a likelihood of exploitation of software vulnerabilities, comprising:
utilizing a processor in operable communication with at least one memory for storing instructions that are executed by the processor to perform operations, including:
accessing a plurality of datasets associated with a predetermined set of data sources, the plurality of datasets including training data comprising hacker communications;
accessing features from the plurality of datasets that include measures computed from social connections of users posting hacking-related content
applying learning algorithms to the training data to generate classification models that are configured to predict class labels defining a likelihood of exploitation of respective software vulnerabilities;
accessing one or more features associated with a software vulnerability; and
computing, by applying the one or more features to the classification model, a class label defining one or more values defining a likelihood of exploitation associated with the software vulnerability, wherein the likelihood of exploitation predicts an actual exploitation of the respective software vulnerabilities before disclosure based on the hacker communications from the training data.