By Ioannis Askoxylakis, Sotiris Ioannidis, Sokratis Katsikas, Catherine Meadows
The two-volume set, LNCS 9878 and 9879 constitutes the refereed complaints of the twenty first ecu Symposium on study in machine safeguard, ESORICS 2016, held in Heraklion, Greece, in September 2016.
The 60 revised complete papers provided have been rigorously reviewed and chosen from 285 submissions. The papers hide quite a lot of subject matters in protection and privateness, together with info safeguard: structures safety, community safety, entry keep watch over, authentication, and safeguard in such rising parts as cloud computing, cyber-physical platforms, and the web of Things.
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Example text
5. Timeliness of blacklists days between the earliest date the SMS-spam domain was seen on a blacklist and the earliest date the domain was seen in an SMS-spam message in our complaint repository. Δ2 (d) measures the difference in days between the earliest date the domain was seen in a blacklist and the earliest date it was looked up, according to the passive DNS visibility we obtained. Figure 5(a) shows the empirical cumulative distribution (eCDF) of Δ1 over all blacklisted domains. We show two plots, one for the FQDNs and the other for the e2LDs.
The key building block of most of these defenses is link padding. Link padding adds varying amounts of delays and dummy messages to the packet flows to conceal patterns in network traffic. Given that bandwidth and latency increases come at a cost to usability and deployability, these defenses must strive for a tradeoff between security and performance overheads. Unfortunately, the state-ofthe-art link-padding defenses are not acceptable for use in Tor: they increase latency, delaying page loads between two and four times and impose bandwidth overheads between 40 % [4] and 350 % [8] on average.
This allows to implement the defense as a Pluggable Transport (PT) [21], avoiding modifications in the Tor source code. Note this model is equivalent for a client connecting to the trusted entry guard without a bridge, but in that case the defense would need to be implemented at the guard. Fig. 1. The WF adversary model considering Tor bridges. The objective of the WF adversary is to determine what pages the user downloads over Tor by looking at the network traces. Early works on this problem [10,19] assumed a user model that could only access a small set of pages—an assumption that is unlikely to be met in practice.