Special Issue "Artificial Intelligence for Cybersecurity"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 20 December 2020.

Special Issue Editor

Dr. Amani S. Ibrahim
Website
Guest Editor
School of Information Technology, Deakin University, Geelong, VIC, Australia
Interests: cybersecurity; big data; IoT; AI/ML

Special Issue Information

Dear Colleagues,

Despite the significant increase in cybersecurity solutions investment, organizations are still plagued by security breaches. Artifial Intelligence (AI) and Machine Learning (ML) has taken centre stage in the cybersecurity industry indicating a clear trend in future cyber defence technologies. With today’s ever evolving cyberthreats, AI and ML are used to automate threat detection and response more efficiently than traditional security solutions. With AI stepping into cybersecurity, experts and researchers are trying to use its potential to identify and counteract sophisticated cyber-attacks with minimal human intervention. Implementing basic building blocks of practical AI together with security solutions, facilitates automation and orchestration to build autonomic security solutions that can keep up with the scale, speed, complexity and adaptability of today’s cybersecurity threats. Hence, with all the hype surrounding AI\ML for cybersecurity, one potential question is how it can be utilised to achieve predictive powers to solve different cybersecurity problems in real-world. Implementing AI\ML in cybersecurity has long-standing challenges that require methodological and theoretical handling. AI\ML introduce a new set of problems, challeges, risks and vulnerabilities, when used in real-world, which makes it susceptible to adversarial activity.

This Special Issue is dedicated to publishing cutting-edge research focused on addressing the various fundamental technical open challenges related to implementing AI\ML in the area of cybersecurity to discuss the hype around the ability of AI-powered solutions that claim to “do it all.”

  • Topics of interest include the following:
  • Artificial intelligence and machine learning for cybersecurity
  • Threat intelligence and AIOps
  • Data intellgeince and DataOps
  • Preventing security and data breaches
  • Risk management and threat management
  • Security operation centers management and challenges
  • Threat landscape prediction
  • Adversairal machine learning
  • Threat and risk modelling
  • Log management
  • IoT security
  • Mobile Security
  • Network Security
  • Enterprise security

Dr. Amani S. Ibrahim
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. Electronics 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 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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Research

Open AccessArticle
A Machine Learning Based Two-Stage Wi-Fi Network Intrusion Detection System
Electronics 2020, 9(10), 1689; https://doi.org/10.3390/electronics9101689 - 15 Oct 2020
Abstract
The growth of wireless networks has been remarkable in the last few years. One of the main reasons for this growth is the massive use of portable and stand-alone devices with wireless network connectivity. These devices have become essential on the daily basis [...] Read more.
The growth of wireless networks has been remarkable in the last few years. One of the main reasons for this growth is the massive use of portable and stand-alone devices with wireless network connectivity. These devices have become essential on the daily basis in consumer electronics. As the dependency on wireless networks has increased, the attacks against them over time have increased as well. To detect these attacks, a network intrusion detection system (NIDS) with high accuracy and low detection time is needed. In this work, we propose a machine learning (ML) based wireless network intrusion detection system (WNIDS) for Wi-Fi networks to efficiently detect attacks against them. The proposed WNIDS consists of two stages that work together in a sequence. An ML model is developed for each stage to classify the network records into normal or one of the specific attack classes. We train and validate the ML model for WNIDS using the publicly available Aegean Wi-Fi Intrusion Dataset (AWID). Several feature selection techniques have been considered to identify the best features set for the WNIDS. Our two-stage WNIDS achieves an accuracy of 99.42% for multi-class classification with a reduced set of features. A module for eXplainable Artificial Intelligence (XAI) is implemented as well to understand the influence of features on each type of network traffic records. Full article
(This article belongs to the Special Issue Artificial Intelligence for Cybersecurity)
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