A. Noroozian
Please Note
13 records found
1
To answer this question, we analyze scraped data on the business-to-business cybercrime segments of AlphaBay (2015-2017), consist- ing of 7,543 listings from 1,339 vendors, sold at least 126,934 times. We construct new variables to capture product differentiators and price. We capture the influence of vendor characteristics by identifying five distinct vendor profiles based on latent profile analysis of six properties. We leverage these product and vendor characteristics to empirically predict the performance of cybercrime products, whilst controlling for the lifespan and type of solution. Consistent with earlier insights into carding forums, we identify prevalent product differentiators to be influencing the relative success of a product. While all these product differentiators do correlate significantly with product performance, their explanatory power is lower than that of vendor profiles. When outsourcing, the vendor seems to be of more importance to the buyers than product differentiators. ...
To answer this question, we analyze scraped data on the business-to-business cybercrime segments of AlphaBay (2015-2017), consist- ing of 7,543 listings from 1,339 vendors, sold at least 126,934 times. We construct new variables to capture product differentiators and price. We capture the influence of vendor characteristics by identifying five distinct vendor profiles based on latent profile analysis of six properties. We leverage these product and vendor characteristics to empirically predict the performance of cybercrime products, whilst controlling for the lifespan and type of solution. Consistent with earlier insights into carding forums, we identify prevalent product differentiators to be influencing the relative success of a product. While all these product differentiators do correlate significantly with product performance, their explanatory power is lower than that of vendor profiles. When outsourcing, the vendor seems to be of more importance to the buyers than product differentiators.
Cybercrime after the sunrise
A statistical analysis of DNS abuse in new gTLDs
To enhance competition and choice in the domain name system, ICANN introduced the new gTLD program, which added hundreds of new gTLDs (e.g. .nyc, .io) to the root DNS zone. While the program arguably increased the range of domain names available to consumers, it might also have created new opportunities for cybercriminals. To investigate that, we present the first comparative study of abuse in the domains registered under the new gTLD program and legacy gTLDs (18 in total, such as .com, .org). We combine historical datasets from various sources, including DNS zone files, WHOIS records, passive and active DNS and HTTP measurements, and 11 reputable abuse feeds to study abuse across gTLDs. We find that the new gTLDs appear to have diverted abuse from the legacy gTLDs: while the total number of domains abused for spam remains stable across gTLDs, we observe a growing number of spam domains in new gTLDs which suggests a shift from legacy gTLDs to new gTLDs. Although legacy gTLDs had a rate of 56.9 spam domains per 10,000 registrations (Q4 2016), new gTLDs experienced a rate of 526.6 in the same period-which is almost one order of magnitude higher. In this study, we also analyze the relationship between DNS abuse, operator security indicators and the structural properties of new gTLDs. The results indicate that there is an inverse correlation between abuse and stricter registration policies. Our findings suggest that cybercriminals increasingly prefer to register, rather than hack, domain names and some new gTLDs have become a magnet for malicious actors. ICANN is currently using these results to review the existing anti-abuse safeguards, evaluate their joint effects and to introduce more effective safeguards before an upcoming new gTLD rollout.
Over the years cybercriminals have misused the Domain Name System (DNS) - a critical component of the Internet - to gain profit. Despite this persisting trend, little empirical information about the security of Top-Level Domains (TLDs) and of the overall 'health' of the DNS ecosystem exists. In this paper, we present security metrics for this ecosystem and measure the operational values of such metrics using three representative phishing and malware datasets. We benchmark entire TLDs against the rest of the market. We explicitly distinguish these metrics from the idea of measuring security performance, because the measured values are driven by multiple factors, not just by the performance of the particular market player. We consider two types of security metrics: occurrence of abuse and persistence of abuse. In conjunction, they provide a good understanding of the overall health of a TLD. We demonstrate that attackers abuse a variety of free services with good reputation, affecting not only the reputation of those services, but of entire TLDs. We find that, when normalized by size, old TLDs like.com host more bad content than new generic TLDs. We propose a statistical regression model to analyze how the different properties of TLD intermediaries relate to abuse counts. We find that next to TLD size, abuse is positively associated with domain pricing (i.e. registries who provide free domain registrations witness more abuse). Last but not least, we observe a negative relation between the DNSSEC deployment rate and the count of phishing domains.
A variety of botnets are used in attacks on financial services. Banks and security firms invest a lot of effort in detecting and combating malware-assisted takeover of customer accounts. A critical resource of these botnets is their command-and-control (C&C) infrastructure. Attackers rent or compromise servers to operate their C&C infrastructure. Hosting providers routinely take down C&C servers, but the effectiveness of this mitigation strategy depends on understanding how attackers select the hosting providers to host their servers. Do they prefer, for example, providers who are slow or unwilling in taking down C&Cs? In this paper, we analyze 7 years of data on the C&C servers of botnets that have engaged in attacks on financial services. Our aim is to understand whether attackers prefer certain types of providers or whether their C&Cs are randomly distributed across the whole attack surface of the hosting industry. We extract a set of structural properties of providers to capture the attack surface. We model the distribution of C&Cs across providers and show that the mere size of the provider can explain around 71% of the variance in the number of C&Cs per provider, whereas the rule of law in the country only explains around 1%. We further observe that price, time in business, popularity and ratio of vulnerable websites of providers relate signi ficantly with C&C counts. Finally, we find that the speed with which providers take down C&C domains has only a weak relation with C&C occurrence rates, adding only 1% explained variance. This suggests attackers have little to no preference for providers who allow long-lived C&C domains.
Herding Vulnerable Cats
A Statistical Approach to Disentangle Joint Responsibility for Web Security in Shared Hosting
Apples, oranges and hosting providers
Heterogeneity and security in the hosting market
Hosting services are associated with various security threats, yet the market has barely been studied empirically. Most security research has relied on routing data and equates providers with Autonomous Systems, ignoring the complexity and heterogeneity of the market. To overcome these limitations, we combined passive DNS data with WHOIS data to identify providers and some of their properties. We found 45,434 hosting providers, spread around a median address space size of 1,517 IP addresses. There is surprisingly little consolidation in the market, even though its services seem amenable to economies of scale. We applied cluster analysis on several measurable characteristics of providers. This uncovered a diverse set of business profiles and an indication of what fraction of the market fits each profile. The profiles are associated with significant differences in security performance, as measured by the uptime of phishing sites. This suggests the approach provides an effective way for security researchers to take the heterogeneity of the market into account.