Download e-book for kindle: Computer Security – ESORICS 2011: 16th European Symposium on by Nashad A. Safa, Saikat Sarkar, Reihaneh Safavi-Naini, Majid

By Nashad A. Safa, Saikat Sarkar, Reihaneh Safavi-Naini, Majid Ghaderi (auth.), Vijay Atluri, Claudia Diaz (eds.)

This booklet constitutes the refereed complaints of the sixteenth ecu Symposium on computing device protection, ESORICS 2011, held in Leuven, Belgium, in September 2011. The 36 papers incorporated within the booklet have been rigorously reviewed and chosen from a hundred and fifty five papers. The articles are equipped in topical sections on instant protection; net safeguard; forensics, biometrics and software program safeguard; entry keep watch over; cryptography and protocol research; info circulation, aspect channels, and vulnerability research; usability, belief, and economics of protection and privateness; and privacy.

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Extra resources for Computer Security – ESORICS 2011: 16th European Symposium on Research in Computer Security, Leuven, Belgium, September 12-14,2011. Proceedings

Sample text

M´onica and C. Ribeiro User Backtrack Linux v4 Legitimate AP FON 2201 Firmware: DD-WRT v24 Wired Wireless Campus Network Evil Twin AP FON 2201 Firmware: DD-WRT v24 Monitor OS X Snow Leopard Fig. 6. 667 · 10−4 (b) Probability of packet loss. extra clients. 16 Fig. 7. Traffic profiles and probability of packet loss Determination of λ. We used a six second window to determine the least probable packet lengths. That is: the choice of packet lengths to be used in the watermark sequence (encrypted networks case) is obtained by monitoring network traffic during six seconds, and choosing the k least observed lengths.

We have numerically computed and plotted the probability of collusion attack in Fig 4. In Fig. 4(a) we can see that as long as the users’ locations cannot be tracked by the adversary, for reasonable values of k, the probability of detecting a collusion attack, (PSLDV ) becomes close to one. In the case of all users’ locations are known to the adversary, Fig. 01. As expected, when users’ locations are compromised to the adversary, we need higher trust assumption and larger number of k to detect a collusion attack with high probability.

This user has no knowledge of the infrastructure, and cannot, thus, verify the authenticity of any available access points in the vicinity. We further assume that the user is in radio range of the legitimate access point, where the wired connection to the internet is located. In fact, this scenario describes almost all public locations where a free hotspot is available, such as cafes or airports. In normal operation, a user in the vicinity of a legitimate AP will associate directly to it, and access the internet.

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