document: Beyond heuristics : learning to classify vulnerabilities and predict exploits The security demands on modern system administration are enormous and getting worse . Chief among these demands , administrators must monitor the continual ongoing disclosure of software vulnerabilities that have the potential to compromise their systems in some way . Such vulnerabilities include buffer overflow errors , improperly validated inputs , and other unanticipated attack modalities . In 2008 , over 7,400 new vulnerabilities were disclosed -- well over 100 per week . While no enterprise is affected by all of these disclosures , administrators commonly face many outstanding vulnerabilities across the software systems they manage . Vulnerabilities can be addressed by patches , reconfigurations , and other workarounds ; however , these actions may incur down-time or unforeseen side-effects . Thus , a key question for systems administrators is which vulnerabilities to prioritize . From publicly available databases that document past vulnerabilities , we show how to train classifiers that predict whether and how soon a vulnerability is likely to be exploited . As input , our classifiers operate on high dimensional feature vectors that we extract from the text fields , time stamps , cross references , and other entries in existing vulnerability disclosure reports . Compared to current industry-standard heuristics based on expert knowledge and static formulas , our classifiers predict much more accurately whether and how soon individual vulnerabilities are likely to be exploited .
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