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Links 1 through 10 of 12 by Luca Bigliardi tagged security

In this paper we experimentally evaluate these issues on a modern automobile and demonstrate the fragility of the underlying system structure. We demonstrate that an attacker who is able to infiltrate any Electronic Control Unit (ECU) can leverage this ability to completely circumvent a broad array of safety-critical systems. Over a range of experiments, we demonstrate the ability to adversarially control a wide range of automotive functions and completely ignore driver input, including disabling the brakes, selectively braking individual wheels on demand, stopping the engine, and so on. We find that it is possible to bypass rudimentary network security protections within the car, such as maliciously bridging between our car’s two internal subnets. We also present composite attacks that leverage individual weaknesses, including an attack that embeds malicious code in a car’s telematics unit and that will completely erase any evidence of its presence after a crash.

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Even if the first RFC that describes this protocol was released in 1995, IPv6 is pretty new and we just begin to see researches, books, papers that cover this protocol. Thus IPv6 was my project for this google Summer of Code. More precisely the project, proposed by FreeBSD, covers security of the IPv6 protocol, the initial job was to review the last years IPv6 stack vulnerabilities and saw if they were fixed in the KAME IPv6 stack used by FreeBSD but I extended the project by trying to find new vulnerabilities, new attacks and so on. This paper tries to give an overview of the work made.

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Operators of online social networks are increasingly sharing potentially
sensitive information about users and their relationships with advertisers,
application developers, and data-mining researchers. Privacy is typically
protected by anonymization, i.e., removing names, addresses, etc.
We present a framework for analyzing privacy and anonymity in social networks
and develop a new re-identification algorithm targeting anonymized social-
network graphs. To demonstrate its effectiveness on real- world networks, we
show that a third of the users who can be verified to have accounts on both
Twitter and Flickr can be re-identified in the anonymous Twitter graph with only
a 12% error rate.
Our de-anonymization algorithm is based purely on the network topology, does
not require creation of a large number of dummy “sybil” nodes, is robust to
noise and all existing defenses, and works even when the overlap between the
target network and the adversary’s auxiliary information is small.

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