137,540 research outputs found
Identifying hidden contexts
In this study we investigate how to identify hidden contexts from the data in classification tasks.
Contexts are artifacts in the data, which do not predict the class label directly.
For instance, in speech recognition task speakers might have different accents, which do not directly discriminate between the spoken words.
Identifying hidden contexts is considered as data preprocessing task, which can help to build more accurate classifiers, tailored for particular contexts and give an insight into the data structure.
We present three techniques to identify hidden contexts, which hide class label information from the input data and partition it using clustering techniques.
We form a collection of performance measures to ensure that the resulting contexts are valid.
We evaluate the performance of the proposed techniques on thirty real datasets.
We present a case study illustrating how the identified contexts can be used to build specialized more accurate classifiers
No NAT'd User left Behind: Fingerprinting Users behind NAT from NetFlow Records alone
It is generally recognized that the traffic generated by an individual
connected to a network acts as his biometric signature. Several tools exploit
this fact to fingerprint and monitor users. Often, though, these tools assume
to access the entire traffic, including IP addresses and payloads. This is not
feasible on the grounds that both performance and privacy would be negatively
affected. In reality, most ISPs convert user traffic into NetFlow records for a
concise representation that does not include, for instance, any payloads. More
importantly, large and distributed networks are usually NAT'd, thus a few IP
addresses may be associated to thousands of users. We devised a new
fingerprinting framework that overcomes these hurdles. Our system is able to
analyze a huge amount of network traffic represented as NetFlows, with the
intent to track people. It does so by accurately inferring when users are
connected to the network and which IP addresses they are using, even though
thousands of users are hidden behind NAT. Our prototype implementation was
deployed and tested within an existing large metropolitan WiFi network serving
about 200,000 users, with an average load of more than 1,000 users
simultaneously connected behind 2 NAT'd IP addresses only. Our solution turned
out to be very effective, with an accuracy greater than 90%. We also devised
new tools and refined existing ones that may be applied to other contexts
related to NetFlow analysis
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