An introduction to variable and feature selection
The Journal of Machine Learning Research
Remote Physical Device Fingerprinting
IEEE Transactions on Dependable and Secure Computing
Automated Traffic Classification and Application Identification using Machine Learning
LCN '05 Proceedings of the The IEEE Conference on Local Computer Networks 30th Anniversary
Traffic classification using clustering algorithms
Proceedings of the 2006 SIGCOMM workshop on Mining network data
Inferring the source of encrypted HTTP connections
Proceedings of the 13th ACM conference on Computer and communications security
Passive data link layer 802.11 wireless device driver fingerprinting
USENIX-SS'06 Proceedings of the 15th conference on USENIX Security Symposium - Volume 15
Identifying unique devices through wireless fingerprinting
WiSec '08 Proceedings of the first ACM conference on Wireless network security
Devices that tell on you: privacy trends in consumer ubiquitous computing
SS'07 Proceedings of 16th USENIX Security Symposium on USENIX Security Symposium
Spot Me if You Can: Uncovering Spoken Phrases in Encrypted VoIP Conversations
SP '08 Proceedings of the 2008 IEEE Symposium on Security and Privacy
Wireless device identification with radiometric signatures
Proceedings of the 14th ACM international conference on Mobile computing and networking
A first look at traffic on smartphones
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
Inferring users' online activities through traffic analysis
Proceedings of the fourth ACM conference on Wireless network security
Tag size does matter: attacks and proofs for the TLS record protocol
ASIACRYPT'11 Proceedings of the 17th international conference on The Theory and Application of Cryptology and Information Security
Peek-a-Boo, I Still See You: Why Efficient Traffic Analysis Countermeasures Fail
SP '12 Proceedings of the 2012 IEEE Symposium on Security and Privacy
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The overall network traffic patterns generated by today's smartphones result from the typically large and diverse set of installed applications. In addition to the traffic generated by the user, most applications generate characteristic traffic from their background activities, such as periodic update requests or server synchronisation. Although the encryption of transmitted data in 3G networks prevents an eavesdropper from analysing the content, periodic traffic patterns leak side-channel information like timing and data volume. In this work, we extract such side-channel features from network traffic generated from the most popular applications, such as Facebook, WhatsApp, Skype, Dropbox, and others, and evaluate whether they can be used to reliably identify a smartphone. By computing fingerprints from approx,6,hours of background traffic, we show that 15 minutes of monitored traffic suffice to reliably identify a smartphone based on its behavioural fingerprint with a success probability of 90%.