Special Issue "Selected Papers from the 3rd International Conference on Machine Learning for Networking (MLN'2020)"

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: closed (30 April 2021).

Special Issue Editors

Prof. Dr. Eric Renault
E-Mail Website
Guest Editor
Institut Mines-Télécom, Télécom SudParis, 91000 Évry, France
Interests: HPC; cluster, grid and cloud computing; 5G networks; MANETs and VANETs; security
Dr. Selma Boumerdassi
E-Mail Website
Guest Editor
Conservatoire National des Arts et Métiers, 75003 Paris, France
Interests: networks; wireless networks; VANETs
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

MLN 2020 is the third edition of the International Conference on Machine Learning for Networking. The goal of the conference is to provide a forum for scientists, engineers, and researchers to discuss and exchange novel ideas, results, experiences, and works-in-process on all aspects of machine learning and networking. Each year, MLN attendees appreciate and benefit from multidisciplinary exchanges on these hot topics. MLN 2020 will be held virtually from 24th to 26th November 2020. For more information about the conference, please use this link: http://adda-association.org/mln-2020/.

Authors of selected papers presented at the conference are invited to submit their extended versions to this Special Issue of the journal Computers after the conference. All submitted papers will undergo our standard peer-review procedure. Accepted papers will be published in open access format in Computers and collected together in this Special Issue website.

The conference paper should be cited and noted on the first page of the paper; authors are asked to disclose that it is a conference paper in their cover letter and include a statement on what has been changed compared to the original conference paper; submitted papers should be extended to the size of regular research or review articles with at least a 50% extension of new results, and papers should not exceed 30% copy/paste from conference papers.

Prof. Dr. Eric Renault
Dr. Selma Boumerdassi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computers is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (4 papers)

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Research

Article
Using Autoencoders for Anomaly Detection and Transfer Learning in IoT
Computers 2021, 10(7), 88; https://0-doi-org.brum.beds.ac.uk/10.3390/computers10070088 - 15 Jul 2021
Viewed by 490
Abstract
With the development of Internet of Things (IoT) technologies, more and more smart devices are connected to the Internet. Since these devices were designed for better connections with each other, very limited security mechanisms have been considered. It would be costly to develop [...] Read more.
With the development of Internet of Things (IoT) technologies, more and more smart devices are connected to the Internet. Since these devices were designed for better connections with each other, very limited security mechanisms have been considered. It would be costly to develop separate security mechanisms for the diverse behaviors in different devices. Given new and changing devices and attacks, it would be helpful if the characteristics of diverse device types could be dynamically learned for better protection. In this paper, we propose a machine learning approach to device type identification through network traffic analysis for anomaly detection in IoT. Firstly, the characteristics of different device types are learned from their generated network packets using supervised learning methods. Secondly, by learning important features from selected device types, we further compare the effects of unsupervised learning methods including One-class SVM, Isolation forest, and autoencoders for dimensionality reduction. Finally, we evaluate the performance of anomaly detection by transfer learning with autoencoders. In our experiments on real data in the target factory, the best performance of device type identification can be achieved by XGBoost with an accuracy of 97.6%. When adopting autoencoders for learning features from the network packets in Modbus TCP protocol, the best F1 score of 98.36% can be achieved. Comparable performance of anomaly detection can be achieved when using autoencoders for transfer learning from the reference dataset in the literature to our target site. This shows the potential of the proposed approach for automatic anomaly detection in smart factories. Further investigation is needed to verify the proposed approach using different types of devices in different IoT environments. Full article
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Article
CBAM: A Contextual Model for Network Anomaly Detection
Computers 2021, 10(6), 79; https://0-doi-org.brum.beds.ac.uk/10.3390/computers10060079 - 11 Jun 2021
Viewed by 846
Abstract
Anomaly-based intrusion detection methods aim to combat the increasing rate of zero-day attacks, however, their success is currently restricted to the detection of high-volume attacks using aggregated traffic features. Recent evaluations show that the current anomaly-based network intrusion detection methods fail to reliably [...] Read more.
Anomaly-based intrusion detection methods aim to combat the increasing rate of zero-day attacks, however, their success is currently restricted to the detection of high-volume attacks using aggregated traffic features. Recent evaluations show that the current anomaly-based network intrusion detection methods fail to reliably detect remote access attacks. These are smaller in volume and often only stand out when compared to their surroundings. Currently, anomaly methods try to detect access attack events mainly as point anomalies and neglect the context they appear in. We present and examine a contextual bidirectional anomaly model (CBAM) based on deep LSTM-networks that is specifically designed to detect such attacks as contextual network anomalies. The model efficiently learns short-term sequential patterns in network flows as conditional event probabilities. Access attacks frequently break these patterns when exploiting vulnerabilities, and can thus be detected as contextual anomalies. We evaluated CBAM on an assembly of three datasets that provide both representative network access attacks, real-life traffic over a long timespan, and traffic from a real-world red-team attack. We contend that this assembly is closer to a potential deployment environment than current NIDS benchmark datasets. We show that, by building a deep model, we are able to reduce the false positive rate to 0.16% while effectively detecting six out of seven access attacks, which is significantly lower than the operational range of other methods. We further demonstrate that short-term flow structures remain stable over long periods of time, making the CBAM robust against concept drift. Full article
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Article
Machine-Learned Recognition of Network Traffic for Optimization through Protocol Selection
Computers 2021, 10(6), 76; https://0-doi-org.brum.beds.ac.uk/10.3390/computers10060076 - 11 Jun 2021
Viewed by 658
Abstract
We introduce optimization through protocol selection (OPS) as a technique to improve bulk-data transfer on shared wide-area networks (WANs). Instead of just fine-tuning the parameters of a network protocol, our empirical results show that the selection of the protocol itself can result in [...] Read more.
We introduce optimization through protocol selection (OPS) as a technique to improve bulk-data transfer on shared wide-area networks (WANs). Instead of just fine-tuning the parameters of a network protocol, our empirical results show that the selection of the protocol itself can result in up to four times higher throughput in some key cases. However, OPS for the foreground traffic (e.g., TCP CUBIC, TCP BBR, UDT) depends on knowledge about the network protocols used by the background traffic (i.e., other users). Therefore, we build and empirically evaluate several machine-learned (ML) classifiers, trained on local round-trip time (RTT) time-series data gathered using active probing, to recognize the mix of network protocols in the background with an accuracy of up to 0.96. Full article
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Article
Indoor–Outdoor Detection in Mobile Networks Using Quantum Machine Learning Approaches
Computers 2021, 10(6), 71; https://0-doi-org.brum.beds.ac.uk/10.3390/computers10060071 - 26 May 2021
Viewed by 963
Abstract
Communication networks are managed more and more by using artificial intelligence. Anomaly detection, network monitoring and user behaviour are areas where machine learning offers advantages over more traditional methods. However, computer power is increasingly becoming a limiting factor in machine learning tasks. The [...] Read more.
Communication networks are managed more and more by using artificial intelligence. Anomaly detection, network monitoring and user behaviour are areas where machine learning offers advantages over more traditional methods. However, computer power is increasingly becoming a limiting factor in machine learning tasks. The rise of quantum computers may be helpful here, especially where machine learning is one of the areas where quantum computers are expected to bring an advantage. This paper proposes and evaluates three approaches for using quantum machine learning for a specific task in mobile networks: indoor–outdoor detection. Where current quantum computers are still limited in scale, we show the potential the approaches have when larger systems become available. Full article
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