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Electronics
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Artificial Intelligence for Cybersecurity
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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: closed (30 October 2021) | Viewed by 55936

Special Issue Editor

Dr. Amani S. Ibrahim

E-Mail 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 submissions that pass pre-check are 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.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 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.

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Published Papers (5 papers)

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Research

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14 pages, 1907 KiB  
Article
Comparing Machine Learning Classifiers for Continuous Authentication on Mobile Devices by Keystroke Dynamics
by Luis de-Marcos, José-Javier Martínez-Herráiz, Javier Junquera-Sánchez, Carlos Cilleruelo and Carmen Pages-Arévalo
Electronics 2021, 10(14), 1622; https://doi.org/10.3390/electronics10141622 - 7 Jul 2021
Cited by 16 | Viewed by 3674
Abstract
Continuous authentication (CA) is the process to verify the user’s identity regularly without their active participation. CA is becoming increasingly important in the mobile environment in which traditional one-time authentication methods are susceptible to attacks, and devices can be subject to loss or [...] Read more.
Continuous authentication (CA) is the process to verify the user’s identity regularly without their active participation. CA is becoming increasingly important in the mobile environment in which traditional one-time authentication methods are susceptible to attacks, and devices can be subject to loss or theft. The existing literature reports CA approaches using various input data from typing events, sensors, gestures, or other user interactions. However, there is significant diversity in the methodology and systems used, to the point that studies differ significantly in the features used, data acquisition, extraction, training, and evaluation. It is, therefore, difficult to establish a reliable basis to compare CA methods. In this study, keystroke mechanics of the public HMOG dataset were used to train seven different machine learning classifiers, including ensemble methods (RFC, ETC, and GBC), instance-based (k-NN), hyperplane optimization (SVM), decision trees (CART), and probabilistic methods (naïve Bayes). The results show that a small number of key events and measurements can be used to return predictions of user identity. Ensemble algorithms outperform others regarding the CA mobile keystroke classification problem, with GBC returning the best statistical results. Full article
(This article belongs to the Special Issue Artificial Intelligence for Cybersecurity)
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18 pages, 9986 KiB  
Article
Optimizing Filter-Based Feature Selection Method Flow for Intrusion Detection System
by Murtaza Ahmed Siddiqi and Wooguil Pak
Electronics 2020, 9(12), 2114; https://doi.org/10.3390/electronics9122114 - 10 Dec 2020
Cited by 47 | Viewed by 6022
Abstract
In recent times, with the advancement in technology and revolution in digital information, networks generate massive amounts of data. Due to the massive and rapid transmission of data, keeping up with security requirements is becoming more challenging. Machine learning (ML)-based intrusion detection systems [...] Read more.
In recent times, with the advancement in technology and revolution in digital information, networks generate massive amounts of data. Due to the massive and rapid transmission of data, keeping up with security requirements is becoming more challenging. Machine learning (ML)-based intrusion detection systems (IDSs) are considered as one of the most suitable solutions for big data security. Despite the progress in ML, unrelated features can drastically influence the performance of an IDS. Feature selection plays a significant role in improving ML-based IDSs. However, the recent growth of dimensionality in data poses quite a challenge for current feature selection and extraction methods. Due to high data dimensionality, feature selection methods suffer in terms of efficiency and effectiveness. In this paper, we are introducing a new process flow for filter-based feature selection with the help of a transformation technique. Generally, normalization or transformation is implemented before classification. In our proposed model, we implemented and evaluated the effects of normalization before feature selection. To present a clear analysis on the effects of power transformation, five different transformations were implemented and evaluated. Furthermore, we implemented and compared different feature selection methods with the proposed process flow. Results show that compared with existing process flow and feature selection methods, our proposed process flow for feature selection can locate a more relevant set of features with high efficiency and accuracy. Full article
(This article belongs to the Special Issue Artificial Intelligence for Cybersecurity)
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27 pages, 323 KiB  
Article
Unknown Security Attack Detection Using Shallow and Deep ANN Classifiers
by Malek Al-Zewairi, Sufyan Almajali and Moussa Ayyash
Electronics 2020, 9(12), 2006; https://doi.org/10.3390/electronics9122006 - 26 Nov 2020
Cited by 28 | Viewed by 3816
Abstract
Advancements in machine learning and artificial intelligence have been widely utilised in the security domain, including but not limited to intrusion detection techniques. With the large training datasets of modern traffic, intelligent algorithms and powerful machine learning tools, security researchers have been able [...] Read more.
Advancements in machine learning and artificial intelligence have been widely utilised in the security domain, including but not limited to intrusion detection techniques. With the large training datasets of modern traffic, intelligent algorithms and powerful machine learning tools, security researchers have been able to greatly improve on the intrusion detection models and enhance their ability to detect malicious traffic more accurately. Nonetheless, the problem of detecting completely unknown security attacks is still an open area of research. The enormous number of newly developed attacks constitutes an eccentric challenge for all types of intrusion detection systems. Additionally, the lack of a standard definition of what constitutes an unknown security attack in the literature and the industry alike adds to the problem. In this paper, the researchers reviewed the studies on detecting unknown attacks over the past 10 years and found that they tended to use inconsistent definitions. This formulates the need for a standard consistent definition to have comparable results. The researchers proposed a new categorisation of two types of unknown attacks, namely Type-A, which represents a completely new category of unknown attacks, and Type-B, which represents unknown attacks within already known categories of attacks. The researchers conducted several experiments and evaluated modern intrusion detection systems based on shallow and deep artificial neural network models and their ability to detect Type-A and Type-B attacks using two well-known benchmark datasets for network intrusion detection. The research problem was studied as both a binary and multi-class classification problem. The results showed that the evaluated models had poor overall generalisation error measures, where the classification error rate in detecting several types of unknown attacks from 92 experiments was 50.09%, which highlights the need for new approaches and techniques to address this problem. Full article
(This article belongs to the Special Issue Artificial Intelligence for Cybersecurity)
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18 pages, 6216 KiB  
Article
A Machine Learning Based Two-Stage Wi-Fi Network Intrusion Detection System
by Abel A. Reyes, Francisco D. Vaca, Gabriel A. Castro Aguayo, Quamar Niyaz and Vijay Devabhaktuni
Electronics 2020, 9(10), 1689; https://doi.org/10.3390/electronics9101689 - 15 Oct 2020
Cited by 36 | Viewed by 5863
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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Review

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27 pages, 5661 KiB  
Review
Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature Review
by Mujaheed Abdullahi, Yahia Baashar, Hitham Alhussian, Ayed Alwadain, Norshakirah Aziz, Luiz Fernando Capretz and Said Jadid Abdulkadir
Electronics 2022, 11(2), 198; https://doi.org/10.3390/electronics11020198 - 10 Jan 2022
Cited by 207 | Viewed by 34703
Abstract
In recent years, technology has advanced to the fourth industrial revolution (Industry 4.0), where the Internet of things (IoTs), fog computing, computer security, and cyberattacks have evolved exponentially on a large scale. The rapid development of IoT devices and networks in various forms [...] Read more.
In recent years, technology has advanced to the fourth industrial revolution (Industry 4.0), where the Internet of things (IoTs), fog computing, computer security, and cyberattacks have evolved exponentially on a large scale. The rapid development of IoT devices and networks in various forms generate enormous amounts of data which in turn demand careful authentication and security. Artificial intelligence (AI) is considered one of the most promising methods for addressing cybersecurity threats and providing security. In this study, we present a systematic literature review (SLR) that categorize, map and survey the existing literature on AI methods used to detect cybersecurity attacks in the IoT environment. The scope of this SLR includes an in-depth investigation on most AI trending techniques in cybersecurity and state-of-art solutions. A systematic search was performed on various electronic databases (SCOPUS, Science Direct, IEEE Xplore, Web of Science, ACM, and MDPI). Out of the identified records, 80 studies published between 2016 and 2021 were selected, surveyed and carefully assessed. This review has explored deep learning (DL) and machine learning (ML) techniques used in IoT security, and their effectiveness in detecting attacks. However, several studies have proposed smart intrusion detection systems (IDS) with intelligent architectural frameworks using AI to overcome the existing security and privacy challenges. It is found that support vector machines (SVM) and random forest (RF) are among the most used methods, due to high accuracy detection another reason may be efficient memory. In addition, other methods also provide better performance such as extreme gradient boosting (XGBoost), neural networks (NN) and recurrent neural networks (RNN). This analysis also provides an insight into the AI roadmap to detect threats based on attack categories. Finally, we present recommendations for potential future investigations. Full article
(This article belongs to the Special Issue Artificial Intelligence for Cybersecurity)
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