A natural language-inspired multilabel video streaming source identification method based on deep neural networks

Shi, Yan; Feng, Dezhi; Cheng, Yu; Biswas, Subir

doi:10.1007/s11760-020-01844-8

Cited by 15 publications

(4 citation statements)

References 16 publications

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“…Given the characteristics of the problem, the multi-label classification task seems to be a suitable path. The work in [61] already demonstrated that multi-label classification could be achieved by a Neural Network-based approach to detect Streaming connections in tunnels. Therefore, this approach will allow us to determine the QoS classes in the multiplexed sessions; however, another interest is to detect explicitly to which class belongs to each packet.…”

Section: Tunneled Traffic Treatmentmentioning

confidence: 99%

A framework to classify heterogeneous Internet traffic with Machine Learning and Deep Learning techniques for satellite communications

Pacheco

Expósito

Gineste³

2020

Computer Networks

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Nowadays, the Internet network system serves as a platform for communication, transaction, and entertainment, among others. This communication system is characterized by terrestrial and Satellite components that interact between themselves to provide transmission paths of information between endpoints. Particularly, Satellite Communication providers' interest is to improve customer satisfaction by optimally exploiting on demand available resources and offering Quality of Service (QoS). Improving the QoS implies to reduce errors linked to information loss and delays of Internet packets in Satellite Communications. In this sense, according to Internet traffic (Streaming, VoIP, Browsing, etc.) and those error conditions, the Internet flows can be classified into different sensitive and non-sensitive classes. Following this idea, this work aims at finding new Internet traffic classification approaches to improving the QoS. Machine Learning (ML) and Deep Learning (DL) techniques will be studied and deployed to classify Internet traffic. All the necessary elements to couple an ML or DL solution over a well-known Satellite Communication and QoS management architecture will be evaluated. To develop this solution, a rich and complete set of Internet traffic is required. In this context, an emulated Satellite Communication platform will serve as a data generation environment in which different Internet communications will be launched and captured. The proposed classification system will deal with different Internet communications (encrypted, unencrypted, and tunneled). This system will process the incoming traffic hierarchically to achieve a high classification performance. Finally, some experiments on a cloud emulated platform validates our proposal and set guidelines for its deployment over a Satellite architecture.

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Section: Tunneled Traffic Treatmentmentioning

confidence: 99%

A framework to classify heterogeneous Internet traffic with Machine Learning and Deep Learning techniques for satellite communications

Pacheco

Expósito

Gineste³

2020

Computer Networks

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“…To exceed the challenges resulting from multi tunnels of traffic, a method of source identification is suggested to identify the multiple sources of video in one encrypted tunnel using DL [52]. The proposed method depends on an image that classifies encrypted traffic of the network with high accuracy, which converts the first few non-zero payload sizes of the session into grayscale images and classifies the converted grayscale images to perform the aim from encrypted network traffic classification by using CNNs [53].…”

Section: DLmentioning

confidence: 99%

A Survey of Intrusion Detection Using Deep Learning in Internet of Things

Farhan

Jasim

2022

ijcsm

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The use of deep learning in various models is a powerful tool in detecting IoT attacks, identifying new types of intrusion to access a better secure network. Need to developing an intrusion detection system to detect and classify attacks in appropriate time and automated manner increases especially due to the use of IoT and the nature of its data that causes increasing in attacks. Malicious attacks are continuously changing, that cause new attacks. In this paper we present a survey about the detection of anomalies, thus intrusion detection by distinguishing between normal behavior and malicious behavior while analyzing network traffic to discover new attacks. This paper surveys previous researches by evaluating their performance through two categories of new datasets of real traffic are (CSE-CIC-IDS2018 dataset, Bot-IoT dataset). To evaluate the performance we show accuracy measurement for detect intrusion in different systems.

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“…Convolutional Neural Networks were used for encrypted video classification by Schuster (2017) and SVM classifiers were applied by Shi (2016). In Shi (2021) the authors used NLP for the video source identification. Wu introduced the differential video fingerprints in Wu (2020) and fuzzy logic for encrypted stream identification was applied by Zu (2016).…”

Section: The Current State Of the Art In Encrypted Streams Detectionmentioning

confidence: 99%

Space Cyber Threats and Need for Enhanced Resilience of Space Assets

2021

Proceedings of the European Conference on Information Warfare and Security

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No reproduction, copy or transmission may be made without written permission from the individual authors. Review ProcessPapers submitted to this conference have been double-blind peer reviewed before final acceptance to the conference. Initially, abstracts were reviewed for relevance and accessibility and successful authors were invited to submit full papers. Many thanks to the reviewers who helped ensure the quality of all the submissions. Ethics and Publication Malpractice PolicyACIL adheres to a strict ethics and publication malpractice policy for all publications -details of which can be found here: http://www.academic-conferences.org/policies/ethics-policy-for-publishing-in-the-conferenceproceedings-of-academic-conferences-and-publishing-international-limited/ Self-Archiving and Paper RepositoriesWe actively encourage authors of papers in ACIL conference proceedings and journals to upload their published papers to university repositories and research bodies such as ResearchGate and Academic.edu. Full reference to the original publication should be provided. Conference ProceedingsThe Conference Proceedings is a book published with an ISBN and ISSN. The proceedings have been submitted to a number of accreditation, citation and indexing bodies including Thomson ISI Web of Science and Elsevier Scopus.

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A natural language-inspired multilabel video streaming source identification method based on deep neural networks

Cited by 15 publications

References 16 publications

A framework to classify heterogeneous Internet traffic with Machine Learning and Deep Learning techniques for satellite communications

A framework to classify heterogeneous Internet traffic with Machine Learning and Deep Learning techniques for satellite communications

A Survey of Intrusion Detection Using Deep Learning in Internet of Things

Space Cyber Threats and Need for Enhanced Resilience of Space Assets

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