Machine Learning - Special issue on learning with probabilistic representations
Functional Trees for Classification
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Behavioral Authentication of Server Flows
ACSAC '03 Proceedings of the 19th Annual Computer Security Applications Conference
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Internet traffic classification using bayesian analysis techniques
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Machine Learning
Traffic classification on the fly
ACM SIGCOMM Computer Communication Review
Inferring the source of encrypted HTTP connections
Proceedings of the 13th ACM conference on Computer and communications security
Tor: the second-generation onion router
SSYM'04 Proceedings of the 13th conference on USENIX Security Symposium - Volume 13
Shining Light in Dark Places: Understanding the Tor Network
PETS '08 Proceedings of the 8th international symposium on Privacy Enhancing Technologies
Internet traffic classification demystified: myths, caveats, and the best practices
CoNEXT '08 Proceedings of the 2008 ACM CoNEXT Conference
On dominant characteristics of residential broadband internet traffic
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Proceedings of the 2009 ACM workshop on Cloud computing security
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Recruiting new tor relays with BRAIDS
Proceedings of the 17th ACM conference on Computer and communications security
An improved algorithm for tor circuit scheduling
Proceedings of the 17th ACM conference on Computer and communications security
Digging into Anonymous Traffic: A Deep Analysis of the Tor Anonymizing Network
NSS '10 Proceedings of the 2010 Fourth International Conference on Network and System Security
One bad apple spoils the bunch: exploiting P2P applications to trace and profile Tor users
LEET'11 Proceedings of the 4th USENIX conference on Large-scale exploits and emergent threats
ExperimenTor: a testbed for safe and realistic tor experimentation
CSET'11 Proceedings of the 4th conference on Cyber security experimentation and test
DefenestraTor: throwing out windows in Tor
PETS'11 Proceedings of the 11th international conference on Privacy enhancing technologies
Tor and circumvention: lessons learned
CRYPTO'11 Proceedings of the 31st annual conference on Advances in cryptology
Website fingerprinting in onion routing based anonymization networks
Proceedings of the 10th annual ACM workshop on Privacy in the electronic society
Proceedings of the 27th Annual Computer Security Applications Conference
Traffic classification using a statistical approach
PAM'05 Proceedings of the 6th international conference on Passive and Active Network Measurement
FC'10 Proceedings of the 14th international conference on Financial Cryptography and Data Security
Tor is unfair -- And what to do about it
LCN '11 Proceedings of the 2011 IEEE 36th Conference on Local Computer Networks
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
Bayesian Neural Networks for Internet Traffic Classification
IEEE Transactions on Neural Networks
Throttling Tor bandwidth parasites
Security'12 Proceedings of the 21st USENIX conference on Security symposium
POSTER: PnP: improving web browsing performance over tor using web resource prefetch-and-push
Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security
PCTCP: per-circuit TCP-over-IPsec transport for anonymous communication overlay networks
Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security
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Tor is a low-latency anonymity-preserving network that enables its users to protect their privacy online. It consists of volunteer-operated routers from all around the world that serve hundreds of thousands of users every day. Due to congestion and a low relay-to-client ratio, Tor suffers from performance issues that can potentially discourage its wider adoption, and result in an overall weaker anonymity to all users. We seek to improve the performance of Tor by defining different classes of service for its traffic. We recognize that although the majority of Tor traffic is interactive web browsing, a relatively small amount of bulk downloading consumes an unfair amount of Tor's scarce bandwidth. Furthermore, these traffic classes have different time and bandwidth constraints; therefore, they should not be given the same Quality of Service (QoS), which Tor offers them today. We propose and evaluate DiffTor, a machine-learning-based approach that classifies Tor's encrypted circuits by application in real time and subsequently assigns distinct classes of service to each application. Our experiments confirm that we are able to classify circuits we generated on the live Tor network with an extremely high accuracy that exceeds 95%. We show that our real-time classification in combination with QoS can considerably improve the experience of Tor clients, as our simple techniques result in a 75% improvement in responsiveness and an 86% reduction in download times at the median for interactive users.