113,667 research outputs found
A Framework for Identifying Malware Threat Distribution on the Dark Web
The Dark Web is an ever-growing phenomenon that has not been deeply explored. It is no secret that in recent years, malware has become a powerful threat to technology users. The Dark Web is known for supporting anonymity and secure connections for private interactions. Over the years, it has become a rich environment for displaying trends, details, and indicators of emerging malware threats. Through the application of data science and open-source intelligence techniques, trends in malware distribution can be studied. In this research, we create a framework for helping identify malware threat distribution patterns. We examine this type of Dark Web activity by utilizing an automated and manual approach for collecting data on malware exchanges. Furthermore, a comparative analysis is conducted to determine which approach is more effective and efficient. Our framework for identifying current or future malware threats that are distributed on the Dark Web is refined by examining the weaknesses and strengths of each gathering approach
Conscript Your Friends into Larger Anonymity Sets with JavaScript
We present the design and prototype implementation of ConScript, a framework
for using JavaScript to allow casual Web users to participate in an anonymous
communication system. When a Web user visits a cooperative Web site, the site
serves a JavaScript application that instructs the browser to create and submit
"dummy" messages into the anonymity system. Users who want to send non-dummy
messages through the anonymity system use a browser plug-in to replace these
dummy messages with real messages. Creating such conscripted anonymity sets can
increase the anonymity set size available to users of remailer, e-voting, and
verifiable shuffle-style anonymity systems. We outline ConScript's
architecture, we address a number of potential attacks against ConScript, and
we discuss the ethical issues related to deploying such a system. Our
implementation results demonstrate the practicality of ConScript: a workstation
running our ConScript prototype JavaScript client generates a dummy message for
a mix-net in 81 milliseconds and it generates a dummy message for a
DoS-resistant DC-net in 156 milliseconds.Comment: An abbreviated version of this paper will appear at the WPES 2013
worksho
Location Privacy in Spatial Crowdsourcing
Spatial crowdsourcing (SC) is a new platform that engages individuals in
collecting and analyzing environmental, social and other spatiotemporal
information. With SC, requesters outsource their spatiotemporal tasks to a set
of workers, who will perform the tasks by physically traveling to the tasks'
locations. This chapter identifies privacy threats toward both workers and
requesters during the two main phases of spatial crowdsourcing, tasking and
reporting. Tasking is the process of identifying which tasks should be assigned
to which workers. This process is handled by a spatial crowdsourcing server
(SC-server). The latter phase is reporting, in which workers travel to the
tasks' locations, complete the tasks and upload their reports to the SC-server.
The challenge is to enable effective and efficient tasking as well as reporting
in SC without disclosing the actual locations of workers (at least until they
agree to perform a task) and the tasks themselves (at least to workers who are
not assigned to those tasks). This chapter aims to provide an overview of the
state-of-the-art in protecting users' location privacy in spatial
crowdsourcing. We provide a comparative study of a diverse set of solutions in
terms of task publishing modes (push vs. pull), problem focuses (tasking and
reporting), threats (server, requester and worker), and underlying technical
approaches (from pseudonymity, cloaking, and perturbation to exchange-based and
encryption-based techniques). The strengths and drawbacks of the techniques are
highlighted, leading to a discussion of open problems and future work
- …