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A General Approach for Traffic Classification in Wireless Networks using Deep Learning [article]

Miguel Camelo, Paola Soto, Steven Latré
2021 Zenodo  
Traffic Classification (TC) systems allow inferring the application that is generating the traffic being analyzed.  ...  State-of-the-art TC algorithms are based on Deep Learning (DL) and have outperformed traditional methods in complex and modern scenarios, even if traffic is encrypted.  ...  However, this feature is not enough to differentiate the L7 application in tasks 2 and 3.  ... 
doi:10.5281/zenodo.5767584 fatcat:doefrvdwxzh7pk5u57etx5rwdu

A General Approach for Traffic Classification in Wireless Networks using Deep Learning

Miguel Camelo, Paola Soto, Steven Latré
2021 Zenodo  
Traffic Classification (TC) systems allow inferring the application that is generating the traffic being analyzed.  ...  State-of-the-art TC algorithms are based on Deep Learning (DL) and have outperformed traditional methods in complex and modern scenarios, even if traffic is encrypted.  ...  However, this feature is not enough to differentiate the L7 application in tasks 2 and 3.  ... 
doi:10.5281/zenodo.5236572 fatcat:nhqriucxcnbf7arvposqz7eq6m

Deep Learning for Encrypted Traffic Classification: An Overview

Shahbaz Rezaei, Xin Liu
2019 IEEE Communications Magazine  
In this article, we introduce a general framework for deep-learning-based traffic classification. We present commonly used deep learning methods and their application in traffic classification tasks.  ...  With the proliferation of deep learning methods, researchers have recently investigated these methods for traffic classification task and reported high accuracy.  ...  TABLE I OVERVIEW I OF DEEP LEARNING METHODS USED FOR TRAFFIC CLASSIFICATION.  ... 
doi:10.1109/mcom.2019.1800819 fatcat:7jxq62uvsbfzljiv5mrgaqyy3q

Segregation of IoT Traffic with Machine Learning Techniques

Shilpa P Khedkar, Et. al.
2021 Turkish Journal of Computer and Mathematics Education  
A proficient classification mechanism in IoT environment should be capable enough to classify the heavy traffic in a fast manner, to deflect the malevolent traffic on time and to transmit the benign traffic  ...  In this manuscript, machine learning and deep neural networks-based approaches are proposed for segregating the IoT traffic which eventually enhances the throughput of IoT networks and reduces the congestion  ...  It has been observed from Table 3 that the deep learning based classifier produces the most accurate results, bagging classifier is next to deep learning based approach in classifying the traffic on  ... 
doi:10.17762/turcomat.v12i2.1806 fatcat:jbvkavbcwfdwvascjhpvdvb54u

Multitask Learning for Network Traffic Classification [article]

Shahbaz Rezaei, Xin Liu
2020 arXiv   pre-print
Classical machine learning algorithms and deep learning models have been widely used to solve the traffic classification task. However, training such models requires a large amount of labeled data.  ...  To solve this challenge, we reformulate the traffic classification into a multi-task learning framework where bandwidth requirement and duration of a flow are predicted along with the traffic class.  ...  First, capturing a large enough labeled dataset for traffic classification to train a deep model is a time-consuming and cumbersome task [15] .  ... 
arXiv:1906.05248v2 fatcat:ad44s5qlnng27mifltzk7umj5e

Large-scale Mobile App Identification Using Deep Learning [article]

Shahbaz Rezaei, Bryce Kroencke, Xin Liu
2019 arXiv   pre-print
In this paper, we propose a deep learning model for mobile app identification.  ...  With the widespread use of mobile devices, the vast diversity of mobile apps, and the massive adoption of encryption protocols (such as TLS), large-scale traffic classification becomes inevitable and more  ...  In [22] , authors used a combination of classical machine learning and deep learning for classification of five Google services that use QUIC protocol.  ... 
arXiv:1910.02350v1 fatcat:jpomja2thng3dbte4ndcphr7vy

Machine Learning for Networking: Workflow, Advances and Opportunities

Mowei Wang, Yong Cui, Xin Wang, Shihan Xiao, Junchen Jiang
2018 IEEE Network  
Recently, machine learning has been used in every possible field to leverage its amazing power.  ...  This article focuses on the application of Machine Learning techniques for Networking (MLN), which can not only help solve the intractable old network questions but also stimulate new network applications  ...  Acknowledgement This work is supported by NSFC (no. 61422206), TNList and the "863" Program of China (no.2015AA016101).  ... 
doi:10.1109/mnet.2017.1700200 fatcat:6ukyxsnkwjh2zfyfm7a22bg3em

Automated Website Fingerprinting through Deep Learning [article]

Vera Rimmer, Davy Preuveneers, Marc Juarez, Tom Van Goethem, Wouter Joosen
2017 arXiv   pre-print
In our open world evaluation, the most performant deep learning model is 2% more accurate than the state-of-the-art attack.  ...  We conclude that the ability to automatically construct the most relevant traffic features and perform accurate traffic recognition makes our deep learning based approach an efficient, flexible and robust  ...  While deep learning allows us to obviate the cumbersome feature engineering process, the learning method does not produce an explicit representation of the features that can be easily interpreted by a  ... 
arXiv:1708.06376v1 fatcat:taqecgcf7rgzxeu3yhfc6ucmne

DeepMAL – Deep Learning Models for Malware Traffic Detection and Classification [article]

Gonzalo Marín, Pedro Casas, Germán Capdehourat
2020 arXiv   pre-print
Deep Learning (DL) models can solve this limitation using their ability to learn feature representations from raw, non-processed data.  ...  In this paper we explore the power of DL models on the specific problem of detection and classification of malware network traffic.  ...  Not surprisingly, the RF using expert domain features achieves highly accurate detection performance, detecting about 97% of all the malware flows with less than 1% of false alarms.  ... 
arXiv:2003.04079v2 fatcat:z7fwmizkf5hz5aoqmdouhoceje

Deep Learning for Large-Scale Traffic-Sign Detection and Recognition [article]

Domen Tabernik, Danijel Skočaj
2019 arXiv   pre-print
Results are reported on highly challenging traffic-sign categories that have not yet been considered in previous works.  ...  We provide comprehensive analysis of the deep learning method for the detection of traffic signs with large intra-category appearance variation and show below 3% error rates with the proposed approach,  ...  The same network architecture is used not only for the TSR but also for accurate localization using a region proposal network, resulting in efficient end-toend learning.  ... 
arXiv:1904.00649v1 fatcat:e5wle6uzubdtvg2jjw6gmz4j3q

Deep-Full-Range: a Deep Learning Based Network Encrypted Traffic Classification and Intrusion Detection Framework

Yi Zeng, Huaxi Gu, Wenting Wei, Yantao Guo
2019 IEEE Access  
Thanks to deep learning, DFR is able to learn from raw traffic without manual intervention and private information.  ...  INDEX TERMS Encrypted traffic classification, network intrusion detection, deep learning, end-to-end. 45182 2169-3536  ...  Previous methods of traffic classification, like the Port Number Based method and the Data Packet Inspection (DPI) Based method [1] , are not competent enough for modern traffic environment due to their  ... 
doi:10.1109/access.2019.2908225 fatcat:ujyaa6mddbcefjx6f7dtgo5gdu

A Hybrid Method for Traffic Flow Forecasting Using Multimodal Deep Learning [article]

Shengdong Du, Tianrui Li, Xun Gong, Shi-Jinn Horng
2019 arXiv   pre-print
The experimental results indicate that the proposed multimodal deep learning model is capable of dealing with complex nonlinear urban traffic flow forecasting with satisfying accuracy and effectiveness  ...  interdependence of multi-modality traffic data by an attention auxiliary multimodal deep learning architecture.  ...  Although the prediction performance of LSTM is excellent, but it is not accurate enough to predict the peak and trough values of the traffic flow as our proposed method.  ... 
arXiv:1803.02099v4 fatcat:aoqv6lmyozfyxlp245zl7ndybi

A Deep Learning Model for Traffic Flow State Classification Based on Smart Phone Sensor Data [article]

Wenwen Tu, Feng Xiao, Liping Fu, Guangyuan Pan
2017 arXiv   pre-print
The result shows that the proposed Deep Belief Network model is superior to traditional machine learning methods in both classification performance and computational efficiency.  ...  This study proposes a Deep Belief Network model to classify traffic flow states.  ...  Acknowledgements The research is supported by the National Natural Science Foundation of China (71622007, 71431003) and a grant from the Fundamental Research Funds for the Central Universities (JBK170501  ... 
arXiv:1709.08802v1 fatcat:h52w3g5bxjhhlpcwzcnivev6cq

Machine Learning for Vehicular Networks [article]

Hao Ye, Le Liang, Geoffrey Ye Li, JoonBeom Kim, Lu Lu, May Wu
2018 arXiv   pre-print
After a brief overview of the major concept of machine learning, we present some application examples of machine learning in solving problems arising in vehicular networks.  ...  In this article, we review recent advances in applying machine learning in vehicular networks and attempt to bring more attention to this emerging area.  ...  Unsupervised Learning For supervised learning, with enough data, the error rate can be reduced close to the minimum error rate bound.  ... 
arXiv:1712.07143v2 fatcat:wf5eupa4o5bwlekunltohu2hli

Deep Learning for Spatio-Temporal Data Mining: A Survey [article]

Senzhang Wang, Jiannong Cao, Philip S. Yu
2019 arXiv   pre-print
predictive learning, representation learning, anomaly detection and classification.  ...  We first categorize the types of spatio-temporal data and briefly introduce the popular deep learning models that are used in STDM.  ...  deep learning model for STDM tasks is still not well studied and remains an open problem.  ... 
arXiv:1906.04928v2 fatcat:4zrdtgkvirfuniq3rb2gl7ohpy
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