Global Internet users increasingly rely on virtual private network (VPN) services to preserve their privacy, circumvent censorship, and access geo-filtered content. Due to their own lack of technical sophistication and the opaque nature of VPN clients, however, the vast majority of users have limited means to verify a given VPN service's claims along any of these dimensions. We design an active measurement system to test various infrastructural and privacy aspects of VPN services and evaluate 62 commercial providers. Our results suggest that while commercial VPN services seem, on the whole, less likely to intercept or tamper with user traffic than other, previously studied forms of traffic proxying, many VPNs do leak user traffic-perhaps inadvertently-through a variety of means. We also find that a non-trivial fraction of VPN providers transparently proxy traffic, and many misrepresent the physical location of their vantage points: 5-30% of the vantage points, associated with 10% of the providers we study, appear to be hosted on servers located in countries other than those advertised to users.

While we have a good understanding of how cybercrime is perpetrated and the profits of the attackers, the harm experienced by humans is less well understood, and reducing this harm should be the ultimate goal of any security intervention. This paper presents a strategy for quantifying the harm caused by the cybercrime of typosquatting via the novel technique of intent inference. Intent inference allows us to define a new metric for quantifying harm to users, develop a new methodology for identifying typosquatting domain names, and quantify the harm caused by various typosquatting perpetrators. We find that typosquatting costs the typical user 1.3 seconds per typosquatting event over the alternative of receiving a browser error page, and legitimate sites lose approximately 5% of their mistyped traffic over the alternative of an unregistered typo. Although on average perpetrators increase the time it takes a user to find their intended site, many typosquatters actually improve the latency between a typo and its correction, calling into question the necessity of harsh penalties or legal intervention against this flavor of cybercrime.

No abstract

With the advent of big data, modern businesses face an increasing need to store and process large volumes of sensitive customer information on the cloud. In these environments, resources are shared across a multitude of mutually untrusting tenants increasing propensity for data leakage. With the recent spate of high-profile data exfiltration attacks and the emergence of critical vulnerabilities such as Heartbleed and Shellshock, coupled with increasing use of clouds in all aspects of our daily lives, this problem stands to grow further in severity. In this thesis, we present a novel network-based covert channel that can arise in the context of shared network resources in data-center environments even in the presence of network monitors regulating flow destinations with NAC policies and VLAN-based isolation mechanisms. Through a series of experiments on diverse network hardware (including SDNs) and commercial clouds such as EC2 and Azure, we demonstrate that our network-based channel achieves orders of magnitude greater bit rates than reported in any recent literature. Furthermore, we present an information-theoretic framework to model and study the channel. Using this model we derive an upper bound on the information rate of the channel and propose a coding scheme that nearly achieves this upper bound. Additionally we introduce some techniques to make the covert channel robust to noise, and empirically study its performance in the presence of realistic crosstraffic. Finally, we discuss several avenues for mitigation, and demonstrate the effectiveness of our schemes both empirically and mathematically.

Users have accumulated years of personal data in cloud storage, creating potential privacy and security risks. This agglomeration includes files retained or shared with others simply out of momentum, rather than intention. We presented 100 online-survey participants with a stratified sample of 10 files currently stored in their own Dropbox or Google Drive accounts. We asked about the origin of each file, whether the participant remembered that file was stored there, and, when applicable, about that file's sharing status. We also recorded participants' preferences moving forward for keeping, deleting, or encrypting those files, as well as adjusting sharing settings. Participants had forgotten that half of the files they saw were in the cloud. Overall, 83% of participants wanted to delete at least one file they saw, while 13% wanted to unshare at least one file. Our combined results suggest directions for retrospective cloud data management.