(U.S.) Rule-based policies for mitigating software risk suggest using the CVSS score to measure the risk of an individual vulnerability and act accordingly. A key issue is whether the ‘danger’ score does actually match the risk of exploitation in the wild, and if and how such a score could be improved. To address this question, we propose using a case-control study methodology similar to the procedure used to link lung cancer and smoking in the 1950s. A case-control study allows the researcher to draw conclusions on the relation between some risk factor (e.g., smoking) and an effect (e.g., cancer) by looking backward at the cases (e.g., patients) and comparing them with controls (e.g., randomly selected patients with similar characteristics). The methodology allows us to quantify the risk reduction achievable by acting on the risk factor. We illustrate the methodology by using publicly available data on vulnerabilities, exploits, and exploits in the wild to (1) evaluate the performances of the current risk factor in the industry, the CVSS base score; (2) determine whether it can be improved by considering additional factors such the existence of a proof-of-concept exploit, or of an exploit in the black markets. Our analysis reveals that (a) fixing a vulnerability just because it was assigned a high CVSS score is equivalent to randomly picking vulnerabilities to fix; (b) the existence of proof-of-concept exploits is a significantly better risk factor; (c) fixing in response to exploit presence in black markets yields the largest risk reduction.

Assessing the risks of software vulnerabilities is a key process of software development and security management. This assessment requires to consider multiple factors (technical features, operational environment, involved assets, status of the vulnerability lifecycle, etc.) and may depend from the assessor’s knowledge and skills. In this work, we tackle with an important part of this problem by measuring the accuracy oftechnicalvulnerability assessments by assessors with different level and type of knowledge. We report an experiment to compare how accurately students with different technical education and security professionals are able to assess the severity of software vulnerabilities with the Common Vulnerability Scoring System (v3) industry methodology. Our results could be useful for increasing awareness about the intrinsic subtleties of vulnerability risk assessment and possibly better compliance with regulations. With respect to academic education, professional training and human resources selections our work suggests that measuring the effects of knowledge and expertise on the accuracy of software security assessments is feasible albeit not easy.

Cybercrime activities are supported by infrastructures and services originating from an underground economy. The current understanding of this phenomenon is that the cybercrime economy ought to be fraught with information asymmetry and adverse selection problems. They should make the effects that we observe every day impossible to sustain. In this paper, we show that the market structure and design used by cyber criminals have evolved toward a market design that is similar to legitimate, thriving, online forum markets such as eBay. We illustrate this evolution by comparing the market regulatory mechanisms of two underground forum markets: 1) a failed market for credit cards and other illegal goods and 2) another, extremely active marketplace for vulnerabilities, exploits, and cyber attacks in general. The comparison shows that cybercrime markets evolved from unruly, scam for scammers market mechanisms to mature, regulated mechanisms that greatly favors trade efficiency.

Phishing attacks arrive in high numbers and often spread quickly, meaning that after-the-fact countermeasures such as domain blacklisting are limited in efficacy. Visual similarity-based approaches have the potential of detecting previously unseen phishing webpages. These approaches, however, require identifying the legitimate webpage(s) they reproduce. Existing approaches rely on textual feature analysis for target identification, with misclassification rates of approximately 1%; however, as most websites a user might visit are legitimate, additional research is needed to further reduce classification errors. In this work, we propose a novel method for target identification that relies on both visual features (extracted from a screenshot of the web page) and textual features (extracted from the DOM of the web page) to identify which website a phishing web page is replicating, and assess its effectiveness in detecting phishing websites using data from phishing aggregators such as OpenPhish, PhishTank and PhishStats. Compared to state-of-the-art text-based classifiers, our method reduces the phishing misclassification rate by 67% (from 1.02% to 0.34%), for an accuracy of 99.66%. This work provides a further step forwards toward semi-automated decision support systems for phishing detection.