26,196 research outputs found
Automated Website Fingerprinting through Deep Learning
Several studies have shown that the network traffic that is generated by a
visit to a website over Tor reveals information specific to the website through
the timing and sizes of network packets. By capturing traffic traces between
users and their Tor entry guard, a network eavesdropper can leverage this
meta-data to reveal which website Tor users are visiting. The success of such
attacks heavily depends on the particular set of traffic features that are used
to construct the fingerprint. Typically, these features are manually engineered
and, as such, any change introduced to the Tor network can render these
carefully constructed features ineffective. In this paper, we show that an
adversary can automate the feature engineering process, and thus automatically
deanonymize Tor traffic by applying our novel method based on deep learning. We
collect a dataset comprised of more than three million network traces, which is
the largest dataset of web traffic ever used for website fingerprinting, and
find that the performance achieved by our deep learning approaches is
comparable to known methods which include various research efforts spanning
over multiple years. The obtained success rate exceeds 96% for a closed world
of 100 websites and 94% for our biggest closed world of 900 classes. In our
open world evaluation, the most performant deep learning model is 2% more
accurate than the state-of-the-art attack. Furthermore, we show that the
implicit features automatically learned by our approach are far more resilient
to dynamic changes of web content over time. We conclude that the ability to
automatically construct the most relevant traffic features and perform accurate
traffic recognition makes our deep learning based approach an efficient,
flexible and robust technique for website fingerprinting.Comment: To appear in the 25th Symposium on Network and Distributed System
Security (NDSS 2018
Comparison of chemical clustering methods using graph- and fingerprint-based similarity measures
This paper compares several published methods for clustering chemical structures, using both graph- and fingerprint-based similarity measures. The clusterings from each method were compared to determine the degree of cluster overlap. Each method was also evaluated on how well it grouped structures into clusters possessing a non-trivial substructural commonality. The methods which employ adjustable parameters were tested to determine the stability of each parameter for datasets of varying size and composition. Our experiments suggest that both graph- and fingerprint-based similarity measures can be used effectively for generating chemical clusterings; it is also suggested that the CAST and Yin–Chen methods, suggested recently for the clustering of gene expression patterns, may also prove effective for the clustering of 2D chemical structures
Similarity-based virtual screening using 2D fingerprints
This paper summarises recent work at the University of Sheffield on virtual screening methods that use 2D fingerprint measures of structural similarity. A detailed comparison of a large number of similarity coefficients demonstrates that the well-known Tanimoto coefficient remains the method of choice for the computation of fingerprint-based similarity, despite possessing some inherent biases related to the sizes of the molecules that are being sought. Group fusion involves combining the results of similarity searches based on multiple reference structures and a single similarity measure. We demonstrate the effectiveness of this approach to screening, and also describe an approximate form of group fusion, turbo similarity searching, that can be used when just a single reference structure is available
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