Michel van Eeten

CR
5papers
130citations
Novelty37%
AI Score21

5 Papers

CYMar 24, 2021
Human Factors in Security Research: Lessons Learned from 2008-2018

Mannat Kaur, Michel van Eeten, Marijn Janssen et al.

Instead of only considering technology, computer security research now strives to also take into account the human factor by studying regular users and, to a lesser extent, experts like operators and developers of systems. We focus our analysis on the research on the crucial population of experts, whose human errors can impact many systems at once, and compare it to research on regular users. To understand how far we advanced in the area of human factors, how the field can further mature, and to provide a point of reference for researchers new to this field, we analyzed the past decade of human factors research in security and privacy, identifying 557 relevant publications. Of these, we found 48 publications focused on expert users and analyzed all in depth. For additional insights, we compare them to a stratified sample of 48 end-user studies. In this paper we investigate: (i) The perspective on human factors, and how we can learn from safety science (ii) How and who are the participants recruited, and how this -- as we find -- creates a western-centric perspective (iii) Research objectives, and how to align these with the chosen research methods (iv) How theories can be used to increase rigor in the communities scientific work, including limitations to the use of Grounded Theory, which is often incompletely applied (v) How researchers handle ethical implications, and what we can do to account for them more consistently Although our literature review has limitations, new insights were revealed and avenues for further research identified.

CRAug 22, 2017
Herding Vulnerable Cats: A Statistical Approach to Disentangle Joint Responsibility for Web Security in Shared Hosting

Samaneh Tajalizadehkhoob, Tom van Goethem, Maciej Korczyński et al.

Hosting providers play a key role in fighting web compromise, but their ability to prevent abuse is constrained by the security practices of their own customers. {\em Shared} hosting, offers a unique perspective since customers operate under restricted privileges and providers retain more control over configurations. We present the first empirical analysis of the distribution of web security features and software patching practices in shared hosting providers, the influence of providers on these security practices, and their impact on web compromise rates. We construct provider-level features on the global market for shared hosting -- containing 1,259 providers -- by gathering indicators from 442,684 domains. Exploratory factor analysis of 15 indicators identifies four main latent factors that capture security efforts: content security, webmaster security, web infrastructure security and web application security. We confirm, via a fixed-effect regression model, that providers exert significant influence over the latter two factors, which are both related to the software stack in their hosting environment. Finally, by means of GLM regression analysis of these factors on phishing and malware abuse, we show that the four security and software patching factors explain between 10\% and 19\% of the variance in abuse at providers, after controlling for size. For web-application security for instance, we found that when a provider moves from the bottom 10\% to the best-performing 10\%, it would experience 4 times fewer phishing incidents. We show that providers have influence over patch levels--even higher in the stack, where CMSes can run as client-side software--and that this influence is tied to a substantial reduction in abuse levels.

CRFeb 6, 2017
Rotten Apples or Bad Harvest? What We Are Measuring When We Are Measuring Abuse

Samaneh Tajalizadehkhoob, Rainer Böhme, Carlos Gañán et al.

Internet security and technology policy research regularly uses technical indicators of abuse in order to identify culprits and to tailor mitigation strategies. As a major obstacle, readily available data are often misaligned with actual information needs. They are subject to measurement errors relating to observation, aggregation, attribution, and various sources of heterogeneity. More precise indicators such as size estimates are costly to measure at Internet scale. We address these issues for the case of hosting providers with a statistical model of the abuse data generation process, using phishing sites in hosting networks as a case study. We decompose error sources and then estimate key parameters of the model, controlling for heterogeneity in size and business model. We find that 84\,\% of the variation in abuse counts across 45,358 hosting providers can be explained with structural factors alone. Informed by the fitted model, we systematically select and enrich a subset of 105 homogeneous "statistical twins" with additional explanatory variables, unreasonable to collect for \emph{all} hosting providers. We find that abuse is positively associated with the popularity of websites hosted and with the prevalence of popular content management systems. Moreover, hosting providers who charge higher prices (after controlling for level differences between countries) witness less abuse. These factors together explain a further 77\,\% of the remaining variation, calling into question premature inferences from raw abuse indicators on security efforts of actors, and suggesting the adoption of similar analysis frameworks in all domains where network measurement aims at informing technology policy.

CRDec 12, 2016
Developing Security Reputation Metrics for Hosting Providers

Arman Noroozian, Maciej Korczyński, Samaneh TajalizadehKhoob et al.

Research into cybercrime often points to concentrations of abuse at certain hosting providers. The implication is that these providers are worse in terms of security; some are considered `bad' or even `bullet proof'. Remarkably little work exists on systematically comparing the security performance of providers. Existing metrics typically count instances of abuse and sometimes normalize these counts by taking into account the advertised address space of the provider. None of these attempts have worked through the serious methodological challenges that plague metric design. In this paper we present a systematic approach for metrics development and identify the main challenges: (i) identification of providers, (ii) abuse data coverage and quality, (iii) normalization, (iv) aggregation and (v) metric interpretation. We describe a pragmatic approach to deal with these challenges. In the process, we answer an urgent question posed to us by the Dutch police: `which are the worst providers in our jurisdiction?'. Notwithstanding their limitations, there is a clear need for security metrics for hosting providers in the fight against cybercrime.

CRDec 9, 2016
Evaluating the Impact of AbuseHUB on Botnet Mitigation

Michel van Eeten, Qasim Lone, Giovane Moura et al.

This documents presents the final report of a two-year project to evaluate the impact of AbuseHUB, a Dutch clearinghouse for acquiring and processing abuse data on infected machines. The report was commissioned by the Netherlands Ministry of Economic Affairs, a co-funder of the development of AbuseHUB. AbuseHUB is the initiative of 9 Internet Service Providers, SIDN (the registry for the .nl top-level domain) and Surfnet (the national research and education network operator). The key objective of AbuseHUB is to improve the mitigation of botnets by its members. We set out to assess whether this objective is being reached by analyzing malware infection levels in the networks of AbuseHUB members and comparing them to those of other Internet Service Providers (ISPs). Since AbuseHUB members together comprise over 90 percent of the broadband market in the Netherlands, it also makes sense to compare how the country as a whole has performed compared to other countries. This report complements the baseline measurement report produced in December 2013 and the interim report from March 2015. We are using the same data sources as in the interim report, which is an expanded set compared to the earlier baseline report and to our 2011 study into botnet mitigation in the Netherlands.