Bro: a system for detecting network intruders in real-time
Computer Networks: The International Journal of Computer and Telecommunications Networking
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
BLINC: multilevel traffic classification in the dark
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
ACM SIGCOMM Computer Communication Review
The BSD packet filter: a new architecture for user-level packet capture
USENIX'93 Proceedings of the USENIX Winter 1993 Conference Proceedings on USENIX Winter 1993 Conference Proceedings
ACM SIGCOMM Computer Communication Review
Revealing skype traffic: when randomness plays with you
Proceedings of the 2007 conference on Applications, technologies, architectures, and protocols for computer communications
Early application identification
CoNEXT '06 Proceedings of the 2006 ACM CoNEXT conference
A Machine Learning Approach for Efficient Traffic Classification
MASCOTS '07 Proceedings of the 2007 15th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems
Toward the accurate identification of network applications
PAM'05 Proceedings of the 6th international conference on Passive and Active Network Measurement
Bayesian Neural Networks for Internet Traffic Classification
IEEE Transactions on Neural Networks
NeTraMark: a network traffic classification benchmark
ACM SIGCOMM Computer Communication Review
Early classification of network traffic through multi-classification
TMA'11 Proceedings of the Third international conference on Traffic monitoring and analysis
Using a behaviour knowledge space approach for detecting unknown IP traffic flows
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Uncovering relations between traffic classifiers and anomaly detectors via graph theory
TMA'10 Proceedings of the Second international conference on Traffic Monitoring and Analysis
K-dimensional trees for continuous traffic classification
TMA'10 Proceedings of the Second international conference on Traffic Monitoring and Analysis
Internet access traffic measurement and analysis
TMA'12 Proceedings of the 4th international conference on Traffic Monitoring and Analysis
Statistical traffic classification by boosting support vector machines
Proceedings of the 7th Latin American Networking Conference
Exploiting packet-sampling measurements for traffic characterization and classification
International Journal of Network Management
On pending interest table in named data networking
Proceedings of the eighth ACM/IEEE symposium on Architectures for networking and communications systems
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
Detection and classification of peer-to-peer traffic: A survey
ACM Computing Surveys (CSUR)
An information-theoretical approach to high-speed flow nature identification
IEEE/ACM Transactions on Networking (TON)
Reviewing traffic classification
DataTraffic Monitoring and Analysis
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The research on network traffic classification has recently become very active. The research community, moved by increasing difficulties in the automated identification of network traffic, started to investigate classification approaches alternative to port-based and payload-based techniques. Despite the large quantity of works published in the past few years on this topic, very few implementations targeting alternative approaches have been made available to the community. Moreover, most approaches proposed in literature suffer of problems related to the ability of evaluating and comparing them. In this paper we present a novel community-oriented software for traffic classification called TIE, which aims at becoming a common tool for the fair evaluation and comparison of different techniques and at fostering the sharing of common implementations and data. Moreover, TIE supports the combination of more classification plugins in order to build multi-classifier systems, and its architecture is designed to allow online traffic classification.