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Electronics
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AI and Cybersecurity: Emerging Trends and Key Challenges
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AI and Cybersecurity: Emerging Trends and Key Challenges

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 7511

Special Issue Editors

Prof. Dr. Savitri Bevinakoppa

E-Mail Website
Guest Editor
School of IT, Melbourne Institute of Technology, Melbourne, VIC 3000, Australia
Interests: computer networks; network security; cybersecurity; data analytics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Gang Li

E-Mail Website
Guest Editor
School of Information Technology, Deakin University, Melbourne, VIC 3125, Australia
Interests: data management; data science; machine learning; cybersecurity and privacy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, entitled "AI and Cybersecurity: Emerging Trends and Key Challenges", aims to explore the dynamic intersection of artificial intelligence and cybersecurity, highlighting both their transformative potential and the pressing risks associated with their convergence. As AI technologies become increasingly embedded in digital infrastructures, they offer powerful tools for threat detection, risk assessment, and automated response. However, they also introduce novel vulnerabilities and ethical concerns that demand rigorous scrutiny. This Special Issue invites the submission of original research, reviews, and case studies that address key challenges such as adversarial AI, secure machine learning, privacy-preserving algorithms, and the role that AI plays in cyber defense and resilience. Contributions are encouraged from academia, industry, and the government to foster a multidisciplinary dialogue on securing AI systems and leveraging AI for robust cybersecurity. By showcasing cutting-edge developments and critical perspectives, this Special Issue seeks to advance our understanding and guide future innovation at the nexus of AI and cybersecurity.

Suggested topics of interest for this Special Issue include the following:

  • AI-driven threat detection and response systems;
  • Adversarial machine learning and model robustness;
  • Privacy-preserving AI and federated learning;
  • Secure AI model deployment and lifecycle management;
  • AI in malware analysis and intrusion detection;
  • Ethical and regulatory challenges in AI-based cybersecurity;
  • Explainability and trust in AI for security applications;
  • Cybersecurity for AI systems and data pipelines;
  • Human–AI collaboration in cyber defense;
  • Emerging standards and frameworks for AI security.

We look forward to receiving your contributions.

Prof. Dr. Savitri Bevinakoppa
Prof. Dr. Gang Li
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 250 words) can be sent to the Editorial Office for assessment.

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 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 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.

Keywords

  • artificial intelligence
  • cybersecurity
  • adversarial machine learning
  • threat detection
  • privacy-preserving AI
  • secure AI systems
  • intrusion detection
  • ethical AI

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Published Papers (5 papers)

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34 pages, 2369 KB  
Article
A Smart Proactive Forensic Meta-Model for Smart Homes in Saudi Arabia Using Metamodeling Approaches
by Majid H. Alsulami
Electronics 2025, 14(21), 4319; https://doi.org/10.3390/electronics14214319 - 3 Nov 2025
Viewed by 750
Abstract
The increasing adoption of smart home technologies introduces significant cybersecurity and forensic challenges. This necessitates a shift from traditional reactive digital forensics to a more proactive approach to safeguarding these environments. This research is situated within Saudi Arabia’s ambitious digital transformation, as outlined [...] Read more.
The increasing adoption of smart home technologies introduces significant cybersecurity and forensic challenges. This necessitates a shift from traditional reactive digital forensics to a more proactive approach to safeguarding these environments. This research is situated within Saudi Arabia’s ambitious digital transformation, as outlined in Vision 2030, which promotes the development of smart cities and homes. The unique technological landscape and national initiatives in Saudi Arabia require tailored cybersecurity solutions. Existing models are often too theoretical, generic, or overly specialized, lacking practical validation and comprehensive integration for modern IoT ecosystems. There is a pronounced lack of a scalable, validated framework designed explicitly for proactive digital forensic readiness in smart homes. The study employs a mixed-methodology approach, combining a PRISMA systematic literature review with Design Science Research (DSR) to develop and validate the Smart Proactive Forensic Metamodel for Smart Homes (SPFMSH). The developed SPFMSH was tested against realistic cyberattack scenarios, including unauthorized access and intrusion, data exfiltration, and device hijacking by ransomware. In each scenario, the model demonstrated its capability to proactively detect threats, automatically preserve forensic evidence, and provide structured investigative timelines. This validation proved its effectiveness in transforming security incidents into forensically sound investigations within the Saudi smart home context. SPFMSH delivers a practical, holistic framework that addresses the limitations of previous models, moving beyond theory to offer an implementable solution. Its development is a significant step towards enhancing national cybersecurity resilience and supporting the secure adoption of smart home technologies in alignment with Saudi Vision 2030. Full article
(This article belongs to the Special Issue AI and Cybersecurity: Emerging Trends and Key Challenges)
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19 pages, 16184 KB  
Article
Double-Flow-Based Steganography Without Embedding for Image-to-Image Hiding
by Yunyun Dong, Zhen Wang, Bingbing Song and Wei Zhou
Electronics 2025, 14(21), 4270; https://doi.org/10.3390/electronics14214270 - 30 Oct 2025
Cited by 1 | Viewed by 696
Abstract
As an emerging concept, steganography without embedding (SWE) hides a secret message without directly embedding it into a cover. Thus, SWE has the unique advantage of being immune to typical steganalysis methods and can better protect the secret message from being exposed. However, [...] Read more.
As an emerging concept, steganography without embedding (SWE) hides a secret message without directly embedding it into a cover. Thus, SWE has the unique advantage of being immune to typical steganalysis methods and can better protect the secret message from being exposed. However, existing SWE methods are generally criticized for their poor payload capacity and low fidelity of recovered secret messages. In this paper, we propose a novel steganography-without-embedding technique, named DF-SWE, which addresses the aforementioned drawbacks and produces diverse and natural stego images. Specifically, DF-SWE employs a reversible circulation of double flow to build a reversible bijective transformation between the secret image and the generated stego image. Hence, it provides a way to directly generate stego images from secret images without a cover image. Besides leveraging the invertible property, DF-SWE can invert a secret image from a generated stego image in a nearly lossless manner and increase the fidelity of extracted secret images. To the best of our knowledge, DF-SWE is the first SWE method that can hide multiple images into one image with the same size, significantly enhancing the payload capacity. According to the experimental results, the payload capacity of DF-SWE achieves 24–72 BPP, which is 8000∼16,000 times more compared to its competitors while producing diverse images to minimize the exposure risk. Importantly, DF-SWE can be applied in the steganography of secret images in various domains without requiring training data from the corresponding domains. This domain-agnostic property suggests that DF-SWE can (1) be applied to hiding private data and (2) be deployed in resource-limited systems. Full article
(This article belongs to the Special Issue AI and Cybersecurity: Emerging Trends and Key Challenges)
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29 pages, 632 KB  
Article
ML-PSDFA: A Machine Learning Framework for Synthetic Log Pattern Synthesis in Digital Forensics
by Wafa Alorainy
Electronics 2025, 14(19), 3947; https://doi.org/10.3390/electronics14193947 - 6 Oct 2025
Viewed by 1095
Abstract
This study introduces the Machine Learning (ML)-Driven Pattern Synthesis for Digital Forensics in Synthetic Log Analysis (ML-PSDFA) framework to address critical gaps in digital forensics, including the reliance on real-world data, limited pattern diversity, and forensic integration challenges. A key innovation is the [...] Read more.
This study introduces the Machine Learning (ML)-Driven Pattern Synthesis for Digital Forensics in Synthetic Log Analysis (ML-PSDFA) framework to address critical gaps in digital forensics, including the reliance on real-world data, limited pattern diversity, and forensic integration challenges. A key innovation is the introduction of a novel temporal forensics loss LTFL in the Synthetic Attack Pattern Generator (SAPG), which enhances the preservation of temporal sequences in synthetic logs that are crucial for forensic analysis. The framework employs the SAPG with hybrid seed data (UNSW-NB15 and CICIDS2017) to create 500,000 synthetic log entries using Google Colab, achieving a realism score of 0.96, a temporal consistency score of 0.90, and an entropy of 4.0. The methodology employs a three-layer architecture that integrates data generation, pattern analysis, and forensic training, utilizing TimeGAN, XGBoost classification with hyperparameter tuning via Optuna, and reinforcement learning (RL) to optimize the extraction of evidence. Due to enhanced synthetic data quality and advanced modeling, the results exhibit an average classification precision of 98.5% (best fold 98.7%) 98.5% (best fold 98.7%), outperforming previously reported approaches. Feature importance analysis highlights timestamps (0.40) and event types (0.30), while the RL workflow reduces false positives by 17% over 1000 episodes, aligning with RL benchmarks. The temporal forensics loss improves the realism score from 0.92 to 0.96 and introduces a temporal consistency score of 0.90, demonstrating enhanced forensic relevance. This work presents a scalable and accessible training platform for legally constrained environments, as well as a novel RL-based evidence extraction method. Limitations include a lack of real-system validation and resource constraints. Future work will explore dynamic reward tuning and simulated benchmarks to enhance precision and generalizability. Full article
(This article belongs to the Special Issue AI and Cybersecurity: Emerging Trends and Key Challenges)
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20 pages, 620 KB  
Article
Discriminative Regions and Adversarial Sensitivity in CNN-Based Malware Image Classification
by Anish Roy and Fabio Di Troia
Electronics 2025, 14(19), 3937; https://doi.org/10.3390/electronics14193937 - 4 Oct 2025
Cited by 2 | Viewed by 877
Abstract
The escalating prevalence of malware poses a significant threat to digital infrastructure, demanding robust yet efficient detection methods. In this study, we evaluate multiple Convolutional Neural Network (CNN) architectures, including basic CNN, LeNet, AlexNet, GoogLeNet, and DenseNet, on a dataset of 11,000 malware [...] Read more.
The escalating prevalence of malware poses a significant threat to digital infrastructure, demanding robust yet efficient detection methods. In this study, we evaluate multiple Convolutional Neural Network (CNN) architectures, including basic CNN, LeNet, AlexNet, GoogLeNet, and DenseNet, on a dataset of 11,000 malware images spanning 452 families. Our experiments demonstrate that CNN models can achieve reliable classification performance across both multiclass and binary tasks. However, we also uncover a critical weakness in that even minimal image perturbations, such as pixel modification lower than 1% of the total image pixels, drastically degrade accuracy and reveal CNNs’ fragility in adversarial settings. A key contribution of this work is spatial analysis of malware images, revealing that discriminative features concentrate disproportionately in the bottom-left quadrant. This spatial bias likely reflects semantic structure, as malware payload information often resides near the end of binary files when rasterized. Notably, models trained in this region outperform those trained in other sections, underscoring the importance of spatial awareness in malware classification. Taken together, our results reveal that CNN-based malware classifiers are simultaneously effective and vulnerable to learning strong representations but sensitive to both subtle perturbations and positional bias. These findings highlight the need for future detection systems that integrate robustness to noise with resilience against spatial distortions to ensure reliability in real-world adversarial environments. Full article
(This article belongs to the Special Issue AI and Cybersecurity: Emerging Trends and Key Challenges)
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41 pages, 1386 KB  
Systematic Review
Federated Learning Under Concept Drift: A Systematic Survey of Foundations, Innovations, and Future Research Directions
by Osamah A. Mahdi, Eric Pardede, Savitri Bevinakoppa and Nawfal Ali
Electronics 2025, 14(22), 4480; https://doi.org/10.3390/electronics14224480 - 17 Nov 2025
Viewed by 3346
Abstract
Federated Learning (FL) is revolutionizing Machine Learning (ML) by enabling devices in different locations to collaborate and learn from user-generated data without centralizing it. In dynamic and non-stationary environments like Internet of Things (IoT), Concept Drift (CD) is the phenomenon of data changing/evolving [...] Read more.
Federated Learning (FL) is revolutionizing Machine Learning (ML) by enabling devices in different locations to collaborate and learn from user-generated data without centralizing it. In dynamic and non-stationary environments like Internet of Things (IoT), Concept Drift (CD) is the phenomenon of data changing/evolving over time. Traditional FL frameworks struggle to maintain performance when local data distributions evolve, as they lack mechanisms for detecting and adapting to concept drift. However, the use of FL in such environments, where data changing/evolving continuously and Continual Learning (CL) is required to adapt to concept drift, remains a relatively unexplored area. This study specifically addresses this gap by examining strategies for continuous adaptation within federated systems when faced with non-stationary data. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this study systematically reviews existing literature on FL adaptation to concept drift. To the best of our knowledge, this is the first systematic review that consolidates and reinterprets existing studies under the emerging framework of Federated Drift-Aware Learning (FDAL), bridging Federated and Continual Learning research toward adaptive and drift-resilient federated systems. We conducted an extensive systematic survey, including an analysis of state-of-the-art methods and the latest developments in this field. Our study highlights their strengths, weaknesses, and datasets used, identifies key challenges faced by FL systems in these scenarios, and explores potential future directions. Additionally, we categorize the limitations and future directions into major thematic areas that highlight core gaps and research opportunities. The results of this study will support researchers in overcoming the adaptation challenges that FL systems face when dealing with changing environments due to concept drift and serve as a critical resource for advancing adaptive federated intelligence. Full article
(This article belongs to the Special Issue AI and Cybersecurity: Emerging Trends and Key Challenges)
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