Traffic classification ≈ Traffic classification
View article: Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction
Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction Open
This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describi…
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Network Traffic Classifier With Convolutional and Recurrent Neural Networks for Internet of Things Open
A network traffic classifier (NTC) is an important part of current network monitoring systems, being its task to infer the network service that is currently used by a communication flow (e.g., HTTP and SIP). The detection is based on a num…
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Deep Learning for Encrypted Traffic Classification: An Overview Open
Traffic classification has been studied for two decades and applied to a wide\nrange of applications from QoS provisioning and billing in ISPs to\nsecurity-related applications in firewalls and intrusion detection systems.\nPort-based, dat…
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ProfilIoT Open
In this work we apply machine learning algorithms on network traffic data for accurate identification of IoT devices connected to a network. To train and evaluate the classifier, we collected and labeled network traffic data from nine dist…
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Towards the Deployment of Machine Learning Solutions in Network Traffic Classification: A Systematic Survey Open
International audience
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ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classification Open
Encrypted traffic classification requires discriminative and robust traffic\nrepresentation captured from content-invisible and imbalanced traffic data for\naccurate classification, which is challenging but indispensable to achieve\nnetwor…
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$Deep-Full-Range$ : A Deep Learning Based Network Encrypted Traffic Classification and Intrusion Detection Framework Open
With the rapid evolution of network traffic diversity, the understanding of network traffic has become more pivotal and more formidable. Previously, traffic classification and intrusion detection require a burdensome analyzing of various t…
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Machine learning based mobile malware detection using highly imbalanced network traffic Open
In recent years, the number and variety of malicious mobile apps have increased drastically, especially on Android platform, which brings insurmountable challenges for malicious app detection. Researchers endeavor to discover the traces of…
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Traffic engineering in software-defined networking: Measurement and management Open
As the next generation network architecture, software-defined networking (SDN) has exciting application prospects. Its core idea is to separate the forwarding layer and control layer of network system, where network operators can program p…
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Network traffic classification: Techniques, datasets, and challenges Open
In network traffic classification, it is important to understand the correlation between network traffic and its causal application, protocol, or service group, for example, in facilitating lawful interception, ensuring the quality of serv…
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An Adaptive Multi-Layer Botnet Detection Technique Using Machine Learning Classifiers Open
In recent years, the botnets have been the most common threats to network security since it exploits multiple malicious codes like a worm, Trojans, Rootkit, etc. The botnets have been used to carry phishing links, to perform attacks and pr…
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A Survey of Techniques for Mobile Service Encrypted Traffic Classification Using Deep Learning Open
The rapid adoption of mobile devices has dramatically changed the access to various networking services and led to the explosion of mobile service traffic. Mobile service traffic classification has been a crucial task that attracts strong …
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Network traffic classification for data fusion: A survey Open
Traffic classification groups similar or related traffic data, which is one main stream technique of data fusion in the field of network management and security. With the rapid growth of network users and the emergence of new networking se…
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A multimodal hybrid parallel network intrusion detection model Open
With the rapid growth of Internet data traffic, the means of malicious attack become more diversified. The single modal intrusion detection model cannot fully exploit the rich feature information in the massive network traffic data, result…
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Mathematical validation of proposed machine learning classifier for heterogeneous traffic and anomaly detection Open
The modeling of an efficient classifier is a fundamental issue in automatic training involving a large volume of representative data. Hence, automatic classification is a major task that entails the use of training methods capable of assig…
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Adaptive encrypted traffic fingerprinting with bi-directional dependence Open
Recently, network traffic analysis has been increasingly used in various applications including security, targeted advertisements, and network management. However, data encryption performed on network traffic poses a challenge to these ana…
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Semi-Supervised Encrypted Traffic Classification With Deep Convolutional Generative Adversarial Networks Open
Network traffic classification serves as a building block for important tasks such as security and quality of service management. The field has been studied for a long time, with many techniques such as classical machine learning and deep …
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Data Traffic Classification in Software Defined Networks (SDN) using supervised-learning Open
Traffic classification with accuracy is of prime importance in network activities such as security monitoring, traffic engineering, fault detection, accounting of network usage, billing and for providing differentiation in Quality of Servi…
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IoT Malware Network Traffic Classification using Visual Representation and Deep Learning Open
With the increase of IoT devices and technologies coming into service, Malware has risen as a challenging threat with increased infection rates and levels of sophistication. Without strong security mechanisms, a huge amount of sensitive da…
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A Comparative Study of Traffic Classification Techniques for Smart City Networks Open
Smart city networks involve many applications that impose specific Quality of Service (QoS) requirements, thus representing a challenging scenario for network management. Solutions aiming to guarantee QoS support have not been deployed in …
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Learning to Classify: A Flow-Based Relation Network for Encrypted Traffic Classification Open
As the size and source of network traffic increase, so does the challenge of monitoring and analyzing network traffic. The challenging problems of classifying encrypted traffic are the imbalanced property of network data, the generalizatio…
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Apply machine learning techniques to detect malicious network traffic in cloud computing Open
Computer networks target several kinds of attacks every hour and day; they evolved to make significant risks. They pass new attacks and trends; these attacks target every open port available on the network. Several tools are designed for t…
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TFE-GNN: A Temporal Fusion Encoder Using Graph Neural Networks for Fine-grained Encrypted Traffic Classification Open
Encrypted traffic classification is receiving widespread attention from researchers and industrial companies. However, the existing methods only extract flow-level features, failing to handle short flows because of unreliable statistical p…
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Yet Another Traffic Classifier: A Masked Autoencoder Based Traffic Transformer with Multi-Level Flow Representation Open
Traffic classification is a critical task in network security and management. Recent research has demonstrated the effectiveness of the deep learning-based traffic classification method. However, the following limitations remain: (1) the t…
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Can Android Applications Be Identified Using Only TCP/IP Headers of Their Launch Time Traffic? Open
The ability to identify mobile apps in network traffic has significant implications in many domains, including traffic management, malware detection, and maintaining user privacy. App identification methods in the literature typically use …
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Network Traffic Anomaly Detection via Deep Learning Open
Network intrusion detection is a key pillar towards the sustainability and normal operation of information systems. Complex threat patterns and malicious actors are able to cause severe damages to cyber-systems. In this work, we propose no…
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Multitask Learning for Network Traffic Classification Open
Traffic classification has various applications in today's Internet, from\nresource allocation, billing and QoS purposes in ISPs to firewall and malware\ndetection in clients. Classical machine learning algorithms and deep learning\nmodels…
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Encrypted Network Traffic Classification Using Deep and Parallel Network-in-Network Models Open
Network traffic classification aims to recognize different application or traffic types by analyzing received data packets. This paper presents a neural network model with deep and parallel network-in-network (NIN) structures for classifyi…
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Network Traffic Classification using Machine Learning Techniques over Software Defined Networks Open
Nowadays Internet does not provide an exchange of information between applications and networks, which may results in poor application performance. Concepts such as application-aware networking or network-aware application programming try …
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A Deep Hierarchical Network for Packet-Level Malicious Traffic Detection Open
As an essential part of the network-based intrusion detection systems (IDS), malicious traffic detection using deep learning methods has become a research focus in network intrusion detection. However, even the most advanced IDS available …