Attack Potential in Impact and Complexity
This work addresses vulnerability prioritization for cybersecurity practitioners, offering an incremental improvement over standard patching methods.
The paper tackled the problem of identifying high-risk vulnerabilities by analyzing real attack data to find a trade-off between impact and complexity, resulting in an estimator that reliably predicts attack volume and improves patching policies by focusing on high-potential vulnerabilities while maintaining coverage.
Vulnerability exploitation is reportedly one of the main attack vectors against computer systems. Yet, most vulnerabilities remain unexploited by attackers. It is therefore of central importance to identify vulnerabilities that carry a high `potential for attack'. In this paper we rely on Symantec data on real attacks detected in the wild to identify a trade-off in the Impact and Complexity of a vulnerability, in terms of attacks that it generates; exploiting this effect, we devise a readily computable estimator of the vulnerability's Attack Potential that reliably estimates the expected volume of attacks against the vulnerability. We evaluate our estimator performance against standard patching policies by measuring foiled attacks and demanded workload expressed as the number of vulnerabilities entailed to patch. We show that our estimator significantly improves over standard patching policies by ruling out low-risk vulnerabilities, while maintaining invariant levels of coverage against attacks in the wild. Our estimator can be used as a first aid for vulnerability prioritisation to focus assessment efforts on high-potential vulnerabilities.