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The identification of vulnerabilities is a continuous challenge in softwareprojects. This is due to the evolution of methods that attackers employ as wellas the constant updates to the software, which reveal additional issues. As aresult, new and innovative approaches for the identification of vulnerablesoftware are needed. In this paper, we present VULNERLIZER, which is a novelframework for cross-analysis between vulnerabilities and software libraries. Ituses CVE and software library data together with clustering algorithms togenerate links between vulnerabilities and libraries. In addition, the trainingof the model is conducted in order to reevaluate the generated associations.This is achieved by updating the assigned weights. Finally, the approach isthen evaluated by making the predictions using the CVE data from the test set.The results show that the VULNERLIZER has a great potential in being able topredict future vulnerable libraries based on an initial input CVE entry or asoftware library. The trained model reaches a prediction accuracy of 75% orhigher.

proceedings of the ... annual hawaii international conference on system sciences/proceedings of the annual hawaii international conference on system sciences

VULNERLIZER: Cross-analysis Between Vulnerabilities and Software Libraries