Special Issue "Machine Learning for Cyber-Security"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 30 June 2019

Special Issue Editor

Guest Editor
Dr. Xavier Bellekens

School of Design and Informatics, University of Abertay, Dundee, United Kingdom
Website | E-Mail
Interests: cyber security; computer networks; internet of things; massively parallel architectures

Special Issue Information

Dear Colleagues,

Over the past decade, the rise of new technologies, such as the Internet of Things and associated interfaces, have dramatically increased the attack surface of consumers and critical infrastructure networks. New threats are being discovered on a daily basis making it harder for current solutions to cope with the large amount of data to analyse. Numerous machine learning algorithms have found their ways in the field of cyber-security in order to identify new and unknown malware, improve intrusion detection systems, enhance spam detection, or prevent software exploit to execute.

While these applications of machine learning algorithms have been proven beneficial for the cyber-security industry, they have also highlighted a number of shortcomings, such as the lack of datasets, the inability to learn from small datasets, the cost of the architecture, to name a few. On the other hand, new and emerging algorithms, such as Deep Learning, One-shot Learning, Continuous Learning and Generative Adversarial Networks, have been successfully applied to solve natural language processing, translation tasks, image classification and even deep face recognition. It is therefore crucial to apply these new methods to cyber-security and measure the success of these less-traditional algorithms when applied to cyber-security.

This Special Issue on machine learning for cyber-security is aimed at industrial and academic researcher applying non-traditional methods to solve cyber-security problems. The key areas of this Special Issue include, but are not limited to:

Generative Adversarial Models; One-shot Learning; Continuous Learning; Challenges of Machine Learning for Cyber Security; Strength and Shortcomings of Machine Learning for Cyber-Security; Graph Representation Learning; Scalable Machine Learning for Cyber Security; Neural Graph Learning; Machine Learning Threat Intelligence; Ethics of Machine Learning for Cyber Security Applications

Dr. Xavier Bellekens
Guest Editor

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. Information 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 1000 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.

Keywords

  • machine learning
  • cyber-security
  • intrusion detection systems
  • malware

Published Papers (1 paper)

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Research

Open AccessArticle Anomaly-Based Method for Detecting Multiple Classes of Network Attacks
Information 2019, 10(3), 84; https://doi.org/10.3390/info10030084
Received: 17 January 2019 / Revised: 8 February 2019 / Accepted: 20 February 2019 / Published: 26 February 2019
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Abstract
The article discusses the problem of detecting network attacks on a web server. The attention is focused on two common types of attacks: “denial of service” and “code injection”. A review and an analysis of various attack detection techniques are conducted. A new [...] Read more.
The article discusses the problem of detecting network attacks on a web server. The attention is focused on two common types of attacks: “denial of service” and “code injection”. A review and an analysis of various attack detection techniques are conducted. A new lightweight approach to detect attacks as anomalies is proposed. It is based on recognition of the dynamic response of the web server during requests processing. An autoencoder is implemented for dynamic response anomaly recognition. A case study with the MyBB web server is described. Several flood attacks and SQL injection attack are modeled and successfully detected by the proposed method. The efficiency of the detection algorithm is evaluated, and the advantages and disadvantages of the proposed approach are analyzed. Full article
(This article belongs to the Special Issue Machine Learning for Cyber-Security)
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