While smartphone usage become more and more pervasive, people start also asking to which extent such devices can be maliciously exploited as "tracking devices". The concern is not only related to an adversary taking physical or remote control of the device (e.g., via a malicious app), but also to what a passive adversary (without the above capabilities) can observe from the device communications. Work in this latter direction aimed, for example, at inferring the apps a user has installed on his device, or identifying the presence of a specific user within a network.In this paper, we move a step forward: we investigate to which extent it is feasible to identify the specific actions that a user is doing on his mobile device, by simply eavesdropping the device's network traffic. In particular, we aim at identifying actions like browsing someone's profile on a social network, posting a message on a friend's wall, or sending an email. We design a system that achieves this goal starting from encrypted TCP/IP packets: it works through identification of network flows and application of machine learning techniques. We did a complete implementation of this system and run a thorough set of experiments, which show that it can achieve accuracy and precision higher than 95%, for most of the considered actions.
When the computer security company Hold Security reported that more than 1.2 billion online credentials had been swiped by Russian hackers, many people were worried—and justifiably so. Hold isn’t saying exactly which websites were hit, but with so many credentials stolen, it’s likely that hundreds of millions of ordinary consumers were affected. Some of these…
Provided that USB devices are connected to virtually all computers, the interface standard conquered the world over the past two decades thanks to its versatility; this versatility is also USB’s Achilles heel, since different device classes can plug into the same connectors, one type of device can turn into a more capable or malicious type without the user noticing.
The system provides weak protection against adaptive adversaries: It is possible to conceal knives, guns, and explosives from detection by exploiting properties of the device’s backscatter X-ray technology. We also investigate cyberphysical threats and propose novel attacks that use malicious software and hardware to compromise the the effectiveness, safety, and privacy of the device. Overall, our findings paint a mixed picture of the Secure 1000 that carries lessons for the design, evaluation, and operation of advanced imaging technologies, for the ongoing public debate concerning their use, and for cyberphysical security more broadly.
A vulnerability within the VBoxGuest driver allows an attacker to inject memory they control into an arbitrary location they define. This can be used by an attacker to overwrite HalDispatchTable+0x4 and execute arbitrary code by subsequently calling NtQueryIntervalProfile on Windows XP SP3 systems.