NVD and Exploit-DB are the de facto standard databases used for research on vulnerabilities, and the CVSS score is the standard measure for risk. On open question is whether such databases and scores are actually representative of attacks found in the wild. To address this question we have constructed a database (EKITS) based on the vulnerabilities currently used in exploit kits from the black market and extracted another database of vulnerabilities from Symantec's Threat Database (SYM). Our final conclusion is that the NVD and EDB databases are not a reliable source of information for exploits in the wild, even after controlling for the CVSS and exploitability subscore. An high or medium CVSS score shows only a significant sensitivity (i.e. prediction of attacks in the wild) for vulnerabilities present in exploit kits (EKITS) in the black market. All datasets exhibit a low specificity. Copyright © 2012 ACM.

A preliminary analysis of vulnerability scores for attacks in wild: The EKITS and SYM datasets

Allodi, Luca;Massacci, Fabio
2012-01-01

Abstract

NVD and Exploit-DB are the de facto standard databases used for research on vulnerabilities, and the CVSS score is the standard measure for risk. On open question is whether such databases and scores are actually representative of attacks found in the wild. To address this question we have constructed a database (EKITS) based on the vulnerabilities currently used in exploit kits from the black market and extracted another database of vulnerabilities from Symantec's Threat Database (SYM). Our final conclusion is that the NVD and EDB databases are not a reliable source of information for exploits in the wild, even after controlling for the CVSS and exploitability subscore. An high or medium CVSS score shows only a significant sensitivity (i.e. prediction of attacks in the wild) for vulnerabilities present in exploit kits (EKITS) in the black market. All datasets exhibit a low specificity. Copyright © 2012 ACM.
2012
Proceedings of the 2012 ACM Workshop on Building analysis datasets and gathering experience returns for security
NY
ACM
9781450316613
Allodi, Luca; Massacci, Fabio
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/96582
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