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It is generally recognized that the traffic generated by an individualconnected to a network acts as his biometric signature. Several tools exploitthis fact to fingerprint and monitor users. Often, though, these tools assumeto access the entire traffic, including IP addresses and payloads. This is notfeasible on the grounds that both performance and privacy would be negativelyaffected. In reality, most ISPs convert user traffic into NetFlow records for aconcise representation that does not include, for instance, any payloads. Moreimportantly, large and distributed networks are usually NAT'd, thus a few IPaddresses may be associated to thousands of users. We devised a newfingerprinting framework that overcomes these hurdles. Our system is able toanalyze a huge amount of network traffic represented as NetFlows, with theintent to track people. It does so by accurately inferring when users areconnected to the network and which IP addresses they are using, even thoughthousands of users are hidden behind NAT. Our prototype implementation wasdeployed and tested within an existing large metropolitan WiFi network servingabout 200,000 users, with an average load of more than 1,000 userssimultaneously connected behind 2 NAT'd IP addresses only. Our solution turnedout to be very effective, with an accuracy greater than 90%. We also devisednew tools and refined existing ones that may be applied to other contextsrelated to NetFlow analysis.

No NAT'd User left Behind: Fingerprinting Users behind NAT from NetFlow Records alone