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Special Issues
Cybersecurity and Artificial Intelligence: Current and Future Developments
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Cybersecurity and Artificial Intelligence: Current and Future Developments

A special issue of AI (ISSN 2673-2688).

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 18030

Special Issue Editors

Dr. Sheikh Tahir Bakhsh

E-Mail Website
Guest Editor
Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff CF5 2YB, UK
Interests: cybersecurity; AI; IoT
Dr. Sabeen Tahir

E-Mail Website
Guest Editor
Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff CF5 2YB, UK
Interests: cybersecurity; IoT; fog computing
Dr. Basit Shahzad

E-Mail Website
Guest Editor
Department of Software Engineering, National University of Modern Languages, Islamabad 44000, Pakistan
Interests: blockchain; AI; software engineering

Special Issue Information

Dear Colleagues,

This Special Issue titled "Cybersecurity and Artificial Intelligence: Current and Future Developments" provides a comprehensive exploration of the sophisticated relationship between cybersecurity and artificial intelligence (AI). In the current technological landscape, cyber threats are escalating in sophistication, demanding innovative approaches to reinforce digital defenses. This Special Issue showcases cutting-edge research on AI-driven cybersecurity solutions, emphasizing their efficacy in adapting to evolving threat vectors. Machine learning algorithms and natural language processing techniques are highlighted for their ability to analyze vast datasets and detect patterns, enhancing threat intelligence across diverse industries. However, this collection also critically examines the challenges and ethical considerations associated with the integration of AI in cybersecurity. Issues such as bias in AI algorithms, the potential for malicious use, and privacy concerns are addressed to encourage an understanding of responsible AI deployment. Looking forward, this Special Issue envisions the convergence of quantum computing, the blockchain, and AI, offering insights into the future of proactive threat hunting, automated incident response, and self-healing systems. The interdisciplinary nature of the contributions, spanning computer science, ethics, law, and policy, can stimulate discussion and discourse, making this collection an invaluable resource for academics, practitioners, and policymakers navigating the complex landscape of securing the digital realm in the age of AI. In essence, this Special Issue serves as a collection of knowledge, providing a universal perspective on the current state and future trajectories of the dynamic relationship between cybersecurity and artificial intelligence.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

  1. Adversarial machine learning in cybersecurity;
  2. Integrating AI for threat detection and prevention;
  3. Ethical considerations of AI-driven cybersecurity;
  4. Secure and privacy-preserving AI algorithms;
  5. AI-powered incident response and forensics;
  6. Machine learning for anomaly detection in network traffic;
  7. Role of AI in predictive cyber risk assessment;
  8. Deep learning approaches for malware analysis;
  9. AI-driven authentication and access control;
  10. The intersection of blockchain and AI in cybersecurity;
  11. AI in cybersecurity policy and governance;
  12. Securing IoT devices with artificial intelligence;
  13. Human factors in AI-enhanced cybersecurity;
  14. Cyber threat intelligence using machine learning models;
  15. Explainability and transparency of AI for cybersecurity.

Dr. Sheikh Tahir Bakhsh
Dr. Sabeen Tahir
Dr. Basit Shahzad
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 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.

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. AI 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 1600 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

  • AI-driven cybersecurity
  • ethical considerations
  • future directions
  • interdisciplinary perspectives
  • digital resilience

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

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21 pages, 2574 KiB  
Article
ZTCloudGuard: Zero Trust Context-Aware Access Management Framework to Avoid Medical Errors in the Era of Generative AI and Cloud-Based Health Information Ecosystems
by Khalid Al-hammuri, Fayez Gebali and Awos Kanan
AI 2024, 5(3), 1111-1131; https://doi.org/10.3390/ai5030055 - 8 Jul 2024
Viewed by 2051
Abstract
Managing access between large numbers of distributed medical devices has become a crucial aspect of modern healthcare systems, enabling the establishment of smart hospitals and telehealth infrastructure. However, as telehealth technology continues to evolve and Internet of Things (IoT) devices become more widely [...] Read more.
Managing access between large numbers of distributed medical devices has become a crucial aspect of modern healthcare systems, enabling the establishment of smart hospitals and telehealth infrastructure. However, as telehealth technology continues to evolve and Internet of Things (IoT) devices become more widely used, they are also increasingly exposed to various types of vulnerabilities and medical errors. In healthcare information systems, about 90% of vulnerabilities emerge from medical error and human error. As a result, there is a need for additional research and development of security tools to prevent such attacks. This article proposes a zero-trust-based context-aware framework for managing access to the main components of the cloud ecosystem, including users, devices, and output data. The main goal and benefit of the proposed framework is to build a scoring system to prevent or alleviate medical errors while using distributed medical devices in cloud-based healthcare information systems. The framework has two main scoring criteria to maintain the chain of trust. First, it proposes a critical trust score based on cloud-native microservices for authentication, encryption, logging, and authorizations. Second, a bond trust scoring system is created to assess the real-time semantic and syntactic analysis of attributes stored in a healthcare information system. The analysis is based on a pre-trained machine learning model that generates the semantic and syntactic scores. The framework also takes into account regulatory compliance and user consent in the creation of the scoring system. The advantage of this method is that it applies to any language and adapts to all attributes, as it relies on a language model, not just a set of predefined and limited attributes. The results show a high F1 score of 93.5%, which proves that it is valid for detecting medical errors. Full article
(This article belongs to the Special Issue Cybersecurity and Artificial Intelligence: Current and Future Developments)
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17 pages, 3202 KiB  
Article
Arabic Spam Tweets Classification: A Comprehensive Machine Learning Approach
by Wafa Hussain Hantom and Atta Rahman
AI 2024, 5(3), 1049-1065; https://doi.org/10.3390/ai5030052 - 2 Jul 2024
Cited by 1 | Viewed by 2010
Abstract
Nowadays, one of the most common problems faced by Twitter (also known as X) users, including individuals as well as organizations, is dealing with spam tweets. The problem continues to proliferate due to the increasing popularity and number of users of social media [...] Read more.
Nowadays, one of the most common problems faced by Twitter (also known as X) users, including individuals as well as organizations, is dealing with spam tweets. The problem continues to proliferate due to the increasing popularity and number of users of social media platforms. Due to this overwhelming interest, spammers can post texts, images, and videos containing suspicious links that can be used to spread viruses, rumors, negative marketing, and sarcasm, and potentially hack the user’s information. Spam detection is among the hottest research areas in natural language processing (NLP) and cybersecurity. Several studies have been conducted in this regard, but they mainly focus on the English language. However, Arabic tweet spam detection still has a long way to go, especially emphasizing the diverse dialects other than modern standard Arabic (MSA), since, in the tweets, the standard dialect is seldom used. The situation demands an automated, robust, and efficient Arabic spam tweet detection approach. To address the issue, in this research, various machine learning and deep learning models have been investigated to detect spam tweets in Arabic, including Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB) and Long-Short Term Memory (LSTM). In this regard, we have focused on the words as well as the meaning of the tweet text. Upon several experiments, the proposed models have produced promising results in contrast to the previous approaches for the same and diverse datasets. The results showed that the RF classifier achieved 96.78% and the LSTM classifier achieved 94.56%, followed by the SVM classifier that achieved 82% accuracy. Further, in terms of F1-score, there is an improvement of 21.38%, 19.16% and 5.2% using RF, LSTM and SVM classifiers compared to the schemes with same dataset. Full article
(This article belongs to the Special Issue Cybersecurity and Artificial Intelligence: Current and Future Developments)
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Review

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21 pages, 2273 KiB  
Review
Artificial Intelligence-Driven Facial Image Analysis for the Early Detection of Rare Diseases: Legal, Ethical, Forensic, and Cybersecurity Considerations
by Peter Kováč, Peter Jackuliak, Alexandra Bražinová, Ivan Varga, Michal Aláč, Martin Smatana, Dušan Lovich and Andrej Thurzo
AI 2024, 5(3), 990-1010; https://doi.org/10.3390/ai5030049 - 27 Jun 2024
Cited by 6 | Viewed by 5447
Abstract
This narrative review explores the potential, complexities, and consequences of using artificial intelligence (AI) to screen large government-held facial image databases for the early detection of rare genetic diseases. Government-held facial image databases, combined with the power of artificial intelligence, offer the potential [...] Read more.
This narrative review explores the potential, complexities, and consequences of using artificial intelligence (AI) to screen large government-held facial image databases for the early detection of rare genetic diseases. Government-held facial image databases, combined with the power of artificial intelligence, offer the potential to revolutionize the early diagnosis of rare genetic diseases. AI-powered phenotyping, as exemplified by the Face2Gene app, enables highly accurate genetic assessments from simple photographs. This and similar breakthrough technologies raise significant privacy and ethical concerns about potential government overreach augmented with the power of AI. This paper explores the concept, methods, and legal complexities of AI-based phenotyping within the EU. It highlights the transformative potential of such tools for public health while emphasizing the critical need to balance innovation with the protection of individual privacy and ethical boundaries. This comprehensive overview underscores the urgent need to develop robust safeguards around individual rights while responsibly utilizing AI’s potential for improved healthcare outcomes, including within a forensic context. Furthermore, the intersection of AI and sensitive genetic data necessitates proactive cybersecurity measures. Current and future developments must focus on securing AI models against attacks, ensuring data integrity, and safeguarding the privacy of individuals within this technological landscape. Full article
(This article belongs to the Special Issue Cybersecurity and Artificial Intelligence: Current and Future Developments)
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Other

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21 pages, 401 KiB  
Systematic Review
Enhancing IoT Security in Vehicles: A Comprehensive Review of AI-Driven Solutions for Cyber-Threat Detection
by Rafael Abreu, Emanuel Simão, Carlos Serôdio, Frederico Branco and António Valente
AI 2024, 5(4), 2279-2299; https://doi.org/10.3390/ai5040112 - 6 Nov 2024
Cited by 2 | Viewed by 4451
Abstract
Background: The Internet of Things (IoT) has improved many aspects that have impacted the industry and the people’s daily lives. To begin with, the IoT allows communication to be made across a wide range of devices, from household appliances to industrial machinery. This [...] Read more.
Background: The Internet of Things (IoT) has improved many aspects that have impacted the industry and the people’s daily lives. To begin with, the IoT allows communication to be made across a wide range of devices, from household appliances to industrial machinery. This connectivity allows for a better integration of the pervasive computing, making devices “smart” and capable of interacting with each other and with the corresponding users in a sublime way. However, the widespread adoption of IoT devices has introduced some security challenges, because these devices usually run in environments that have limited resources. As IoT technology becomes more integrated into critical infrastructure and daily life, the need for stronger security measures will increase. These devices are exposed to a variety of cyber-attacks. This literature review synthesizes the current research of artificial intelligence (AI) technologies to improve IoT security. This review addresses key research questions, including: (1) What are the primary challenges and threats that IoT devices face?; (2) How can AI be used to improve IoT security?; (3) What AI techniques are currently being used for this purpose?; and (4) How does applying AI to IoT security differ from traditional methods? Methods: We included a total of 33 peer-reviewed studies published between 2020 and 2024, specifically in journal and conference papers written in English. Studies irrelevant to the use of AI for IoT security, duplicate studies, and articles without full-text access were excluded. The literature search was conducted using scientific databases, including MDPI, ScienceDirect, IEEE Xplore, and SpringerLink. Results were synthesized through a narrative synthesis approach, with the help of the Parsifal tool to organize and visualize key themes and trends. Results: We focus on the use of machine learning, deep learning, and federated learning, which are used for anomaly detection to identify and mitigate the security threats inherent to these devices. AI-driven technologies offer promising solutions for attack detection and predictive analysis, reducing the need for human intervention more significantly. This review acknowledges limitations such as the rapidly evolving nature of IoT technologies, the early-stage development or proprietary nature of many AI techniques, the variable performance of AI models in real-world applications, and potential biases in the search and selection of articles. The risk of bias in this systematic review is moderate. While the study selection and data collection processes are robust, the reliance on narrative synthesis and the limited exploration of potential biases in the selection process introduce some risk. Transparency in funding and conflict of interest reporting reduces bias in those areas. Discussion: The effectiveness of these AI-based approaches can vary depending on the performance of the model and the computational efficiency. In this article, we provide a comprehensive overview of existing AI models applied to IoT security, including machine learning (ML), deep learning (DL), and hybrid approaches. We also examine their role in enhancing the detection accuracy. Despite all the advances, challenges still remain in terms of data privacy and the scalability of AI solutions in IoT security. Conclusion: This review provides a comprehensive overview of ML applications to enhance IoT security. We also discuss and outline future directions, emphasizing the need for collaboration between interested parties and ongoing innovation to address the evolving threat landscape in IoT security. Full article
(This article belongs to the Special Issue Cybersecurity and Artificial Intelligence: Current and Future Developments)
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