Results 201 to 210 of about 28,593 (259)

Multi-stage classification of abnormal traffic events using a multi-head + LSTM. [PDF]

Sci Rep
Jadhav P, Benslimane A, Vora DR, Patil S.   +3 more
europepmc   +1 more source

ASTRID-Net: SE-enhanced triple attention deep learning framework for IoT and IIoT security. [PDF]

Sci Rep
Zannat A, Ahmmed MS, Hossain MA, Islam MS, Manarat AS.   +4 more
europepmc   +1 more source

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Blind Traffic Classification in Wireless Networks

2020 28th European Signal Processing Conference (EUSIPCO), 2021
In this paper, we propose a non-collaborative radiofrequency (RF) sensor network that, observing the radio spectrum generated by the users of a wireless network, can separate and classify their activities. Numerical results demonstrate that using blind source separation (BSS) and some well-known classifiers, over-the-air user traffic identification is ...
Testi, Enrico, Pucci, Lorenzo, Favarelli, Elia, Giorgetti, Andrea   +3 more
openaire   +1 more source

Robust Network Traffic Classification

IEEE/ACM Transactions on Networking, 2015
As a fundamental tool for network management and security, traffic classification has attracted increasing attention in recent years. A significant challenge to the robustness of classification performance comes from zero-day applications previously unknown in traffic classification systems.
Zhang, Jun, Chen, Xiao, Xiang, Yang, Zhou, Wanlei, Wu, Jie   +4 more
openaire   +2 more sources

InFeCT - Network Traffic Classification

Seventh International Conference on Networking (icn 2008), 2008
Network traffic policy verification is the analysis of network traffic to determine if the observed traffic is in compliance or violation of the applied policy. An intuitive approach is the use of machine learning techniques based on specific network traffic characteristics.
Peter Teufl, Udo Payer, Michael Amling, Martin Godec, Stefan Ruff, Gerhard Scheikl, Gernot Walzl   +6 more
openaire   +1 more source

Application identification via network traffic classification

2017 International Conference on Computing, Networking and Communications (ICNC), 2017
Recent developments in Internet technology have led to an increased importance of network traffic classification. In this study, we used machine-learning methods for the identification of applications using network traffic classification. Contrary to existing studies, which classify applications into categories like FTP, Instant Messaging, etc., we ...
Karsligil, M. E., YAMANSAVASCILAR, Baris, YAVUZ, Ali Gökhan, GÜVENSAN, Mehmet Amaç   +3 more
openaire   +2 more sources

Network traffic classification with Self Organizing Maps

2007 22nd international symposium on computer and information sciences, 2007
Anomaly detection in network traffic is one of the most challenging topics in the study of computer science and networking. This paper introduces a classification method for analyzing network traffic behavior. In order to distinguish the normal traffic with well-known anomalies such as port scanning and DOS attacks, Self Organizing Maps (SOMs), one of ...
Kızılören, Tevfik, Germen, Emin
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Self Learning Network Traffic Classification

2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), 2015
Network management is part of traffic engineering and security. The current solutions - Deep Packet Inspection (DPI) and statistical classification, rely on the availability of a training set. In case of these there is a cumbersome need to regularly update the signatures.
null Vandana M, Sruthy Manmadhan
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Bayesian Neural Networks for Internet Traffic Classification

IEEE Transactions on Neural Networks, 2007
Internet traffic identification is an important tool for network management. It allows operators to better predict future traffic matrices and demands, security personnel to detect anomalous behavior, and researchers to develop more realistic traffic models.
Tom, Auld, Andrew W, Moore, Stephen F, Gull   +2 more
openaire   +2 more sources