Special Issue "Machine Learning for Cybersecurity Threats, Challenges, and Opportunities"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 September 2019

Special Issue Editors

Guest Editor
Prof. Luis Javier Garcia Villalba

Group of Analysis, Security and Systems (GASS), Department of Software Engineering and Artificial Intelligence (DISIA), Faculty of Computer Science and Engineering, Office 431, Universidad Complutense de Madrid (UCM), Calle Profesor José García Santesmases, 9, Ciudad Universitaria, 28040 Madrid, Spain
Website | E-Mail
Phone: +34 91 394 76 38
Interests: Anonymity; Computer Security; Cyber Security; Cryptography; Information Security; Intrusion Detection; Malware; Privacy; Trust
Guest Editor
Dr. Mario Blaum

IBM Almaden Research Center, San Jose, CA, USA
Website | E-Mail
Interests: error-correcting codes; fault tolerance; parallel processing; cryptography; modulation codes for magnetic recording; timing algorithms; holographic storage; parallel communications; neural networks; finite group theory
Guest Editor
Dr. Ana Lucila Sandoval Orozco

Cybersecurity INCT Unit 6, Decision Technologies Laboratory-LATITUDE, Electrical Engineering Department (ENE), Technology College, University of Brasília (UnB), Brasília-DF, CEP 70910-900, Brazil
Interests: computer and network security; multimedia forensics; error-correcting codes; information theory

Special Issue Information

Dear Colleagues,

Cybersecurity has become a major priority for every organization. The right controls and procedures must be put in place to detect potential attacks and protect against them. However, the number of cyber-attacks will be always bigger than the number of people trying to protect themselves against attacks. New threats are being discovered on a daily basis, making it harder for current solutions to cope with a large amount of data to analyze. Machine learning systems can be trained to find attacks which are similar to known attacks. This way, we can detect even the first intrusions of their kind and develop better security measures.

The sophistication of threats has also increased substantially. Sophisticated zero-day attacks may go undetected for months at a time. Attack patterns may be engineered to take place over extended periods of time, making them very difficult for traditional intrusion detection technologies to detect. Even worse, new attack tools and strategies can now be developed using adversarial machine learning techniques, requiring a rapid co-evolution of defenses that matches the speed and sophistication of machine learning-based offensive techniques. Based on this motivation, this Special Issue aims at providing a forum for people from academia and industry to communicate their latest results on theoretical advances and industrial case studies that combine machine learning techniques, such as reinforcement learning, adversarial machine learning, and deep learning, with significant problems in cybersecurity. Research papers can be focused on offensive and defensive applications of machine learning to security. The potential topics of interest of this Special Issue are listed below. Submissions can contemplate original research, serious dataset collection and benchmarking, or critical surveys.

Potential topics include but are not limited to:

  • Adversarial training and defensive distillation;
  • Attacks against machine learning;
  • Black-box attacks against machine learning;
  • Challenges of machine learning for cyber security;
  • Ethics of machine learning for cyber security applications;
  • Generative adversarial models;
  • Graph representation learning;
  • Machine learning forensics;
  • Machine learning threat intelligence;
  • Malware detection;
  • Neural graph learning;
  • One-shot learning; continuous learning;
  • Scalable machine learning for cyber security;
  • Steganography and steganalysis based on machine learning techniques;
  • Strength and shortcomings of machine learning for cyber-security.

Prof. Luis Javier Garcia Villalba
Dr. Mario Blaum
Dr. Ana Lucila Sandoval Orozco
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 1500 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 (1 paper)

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Open AccessArticle Network Intrusion Detection Based on Novel Feature Selection Model and Various Recurrent Neural Networks
Appl. Sci. 2019, 9(7), 1392; https://doi.org/10.3390/app9071392
Received: 24 February 2019 / Revised: 28 March 2019 / Accepted: 29 March 2019 / Published: 3 April 2019
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The recent increase in hacks and computer network attacks around the world has intensified the need to develop better intrusion detection and prevention systems. The intrusion detection system (IDS) plays a vital role in detecting anomalies and attacks on the network which have [...] Read more.
The recent increase in hacks and computer network attacks around the world has intensified the need to develop better intrusion detection and prevention systems. The intrusion detection system (IDS) plays a vital role in detecting anomalies and attacks on the network which have become larger and more pervasive in nature. However, most anomaly-based intrusion detection systems are plagued by high false positives. Furthermore, Remote-to-Local (R2L) and User-to-Root (U2R) are two kinds of attack which have low predicted accuracy scores in advance IDS methods. Therefore, this paper proposes a novel IDS framework to overcome these IDS problems. The proposed framework including three main parts. The first part is to build SFSDT model which is the feature selection model. SFSDT is to generate the best feature subset from the original feature set. This model is a hybrid Sequence Forward Selection (SFS) algorithm and Decision Tree (DT) model. The second part is to build various IDS models to train on the best-selected feature subset. The various Recurrent Neural Networks (RNN) are traditional RNN, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). Two IDS datasets are used for the learned models in experiments including NSL-KDD in 2010 and ISCX in 2012. The final part is to evaluate the proposed model by comparing the proposed models to other IDS models. The experimental results show the proposed models achieve significantly improved accuracy detection rate as well as attack types classification. Furthermore, this approach can reduce the computation time by memory profilers measurement. Full article

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