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A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Cybersecurity".

Deadline for manuscript submissions: closed (30 June 2026) | Viewed by 9200

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Editors

Dr. Wei Zong

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Guest Editor

School of Computing and Information Technology, Institute of Cybersecurity and Cryptology, University of Wollongong, Wollongong, NSW 2522, Australia
Interests: deep learning; adversarial machine learning; visualization; cybersecurity

Dr. Yang-Wai Chow

E-Mail Website
Guest Editor

School of Computing and Information Technology, University of Wollongong, Wollongong, NSW 2522, Australia
Interests: virtual reality; multimedia security; adversarial machine learning
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Special Issue Information

Dear Colleagues,

Cybersecurity aims to protect networks, devices, and data from unauthorized access or criminal use and ensure the confidentiality, integrity, and availability of information. With the rapid advancement of artificial intelligence, deploying machine learning (ML) technologies, especially deep learning technologies, for cybersecurity has attracted increasing attention from experts. For instance, deep neural networks are now widely used for network intrusion detection, malware detection, and spam detection. Nonetheless, researchers have shown that adversarial attacks pose practical threats to ML systems. Adversarial examples and backdoor attacks are examples of adversarial attacks against machine learning models that aim to deceive or compromise their performance. Adversarial examples are specially crafted inputs that can fool a model into making incorrect predictions, while backdoor attacks introduce hidden triggers that cause a model to misclassify certain inputs. Another adversarial threat are model stealing attacks, also known as model extraction attacks, which refer to the unauthorized copying or replication of a machine learning model. Taking into consideration these adversarial threats to cybersecurity systems, it is essential to proactively manage and mitigate risks by implementing robust cybersecurity measures.

This Special Issue aims to promote the dissemination of the latest methodologies, solutions, and case studies pertaining to adversarial attacks in cybersecurity. Our objective is to publish high‐quality articles presenting practical adversarial attacks, security algorithms, and solutions for cybersecurity measures. Technical papers describing previously unpublished, original, state‐of‐the‐art research, not currently under review by a conference or journal, will be considered.

Possible topics of interest of this Special Issue include, but are not limited to, the following:

Adversarial examples in cybersecurity;
Backdoor attacks in cybersecurity;
Robust cybersecurity measures;
Model stealing in cybersecurity;
Intellectual property protection in cybersecurity;
AI-powered robust cybersecurity techniques;
Big data-driven robust cybersecurity methods, including analytics and visualization;
Adversarial attacks against cybersecurity systems;
Machine/deep learning methods for robust cybersecurity systems.

Dr. Wei Zong
Dr. Yang-Wai Chow
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet 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 1800 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

cybersecurity
artificial intelligence
big data
machine/deep learning
adversarial attacks
adversarial robustness

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Published Papers (9 papers)

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Research

22 pages, 492 KB

Open AccessArticle

TransForge: A Genetic Algorithm Framework for Cross-Category Evaluation of Endpoint Detection Robustness to Code Transformations

by Alvina Rwaichi Minja and Jema David Ndibwile

Future Internet 2026, 18(7), 356; https://doi.org/10.3390/fi18070356 - 9 Jul 2026

Viewed by 309

Abstract

Endpoint protection systems increasingly rely on a combination of signature-based and behavioral detection mechanisms, yet their robustness under systematic code transformation remains insufficiently understood. This paper presents a multi-category evaluation of endpoint detection robustness under automated, semantic-preserving code transformations across diverse execution variants. We introduce TransForge, a generalized transformation framework designed to generate functionally equivalent execution variants for controlled robustness assessment across heterogeneous artifact categories and programming environments. Building on our prior work, ShellForge, which focused on a single artifact class, TransForge extends this approach to support multi-category analysis through a modular transformation pipeline and an evolutionary strategy that enables non-deterministic variant generation. Using a dataset of 75 base samples spanning six execution categories and four programming languages, we conduct controlled experiments to evaluate how endpoint detection systems respond to systematically generated variants under consistent conditions. Across the evaluated corpus, 53 of 75 evolved variants (70.7%) achieved zero VirusTotal detections across 76 engines, while 94% of variants preserved full functional correctness. Detection outcomes varied substantially by category: staged downloaders exhibited a mean of 8.75 alerts while credential access, surveillance, and cross-site execution variants produced zero alerts under identical evolutionary configuration, with all six categories converging uniformly at generation 4. Statistical analysis using ANOVA, Mann–Whitney U, and Wilcoxon signed-rank tests confirm that these differences are statistically significant (

p < 0.001

) and practically meaningful (Cohen’s

d > 0.8

). The findings highlight coverage gaps in both signature-based and behavioral detection pipelines when faced with semantic-preserving transformations, motivating the development of robustness-aware evaluation frameworks and detection pipelines that leverage behavioral correlation and adaptive analysis beyond static signature matching. Full article

(This article belongs to the Special Issue Adversarial Attacks and Cyber Security)

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23 pages, 311 KB

Open AccessArticle

Evaluating Adversarial Robustness of Deepfake Audio Detectors and Vocoder Fingerprint Detectors Against Universal Adversarial Perturbations

by Quang Minh Tran, Wei Zong, Yang-Wai Chow and Willy Susilo

Future Internet 2026, 18(7), 344; https://doi.org/10.3390/fi18070344 - 29 Jun 2026

Viewed by 326

Abstract

Audio deepfake and vocoder fingerprint detectors are increasingly used to identify synthetic speech and attribute it to its generating model. However, their robustness against adversarial perturbations remains unclear across attack algorithms, perturbation domains, detector representations, and vocoder types. This paper presents a focused, quality-aware evaluation of four representative adversarial attacks, namely the Fast Gradient Sign Method (FGSM), Basic Iterative Method (BIM), Projected Gradient Descent (PGD), and Carlini–Wagner (CW) attack, against audio deepfake and vocoder fingerprint detectors. Each attack is implemented in both the waveform domain and the short-time Fourier transform (STFT) magnitude domain. All attacks are optimized against Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks (AASIST) under a targeted fake-to-real objective and are evaluated on synthetic speech generated by HiFi-GAN, Fullband MelGAN, StyleMelGAN, and Parallel WaveGAN. Attack performance is first measured on the source AASIST detector, after which black-box transferability is assessed on three target detector families: ResNet with Linear Frequency Cepstral Coefficient (LFCC) features, LCNN with Constant-Q Cepstral Coefficient (CQCC) features, and a bidirectional long short-term memory (BiLSTM) detector. The results show that adversarial effectiveness depends strongly on perturbation domain and detector representation. STFT-magnitude PGD transfers strongly to LFCC-based ResNet detectors but has limited effect on CQCC-based and recurrent detectors. In contrast, waveform-domain attacks produce broader transferability across feature-based detectors, with different attacks showing distinct ASR–quality trade-offs. Under the chosen waveform-domain budget, FGSM and BIM preserve transcription-level intelligibility while retaining meaningful black-box transferability, whereas CW provides the strongest overall source-detector and black-box attack performance. To distinguish effective adversarial perturbations from destructive signal degradation, we evaluate audio quality and intelligibility using word error rate (WER) and signal-to-noise ratio (SNR). Overall, the findings show that robustness claims in audio deepfake and vocoder fingerprint detection are limited when adversarial perturbations, black-box transferability, and audio quality are jointly considered. Full article

(This article belongs to the Special Issue Adversarial Attacks and Cyber Security)

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42 pages, 4791 KB

Open AccessArticle

Unpacking Internet-Based Social Engineering Victimisation on Social Networking Sites: An Interdisciplinary Qualitative Framework of Individual, Social, and Platform Factors

by Saad Saleh Alshammari, Ben Soh and Alice Li

Future Internet 2026, 18(7), 336; https://doi.org/10.3390/fi18070336 - 25 Jun 2026

Viewed by 346

Abstract

Despite extensive research on social engineering victimisation on social networking sites (SNSs) across the Internet, user susceptibility continues to increase, indicating that existing explanatory models remain incomplete. Previous studies have predominantly examined susceptibility through isolated factors, including individual traits, message characteristics, or source attributes, while often overlooking how evolving Internet-based SNS environments interact with human and social factors. To address this gap, this study presents an interdisciplinary qualitative investigation into emerging determinants of user susceptibility to social engineering cyberattacks (SECAs) on Internet-enabled SNS platforms. Drawing on in-depth interviews with 18 experts from cybersecurity, psychology, sociology, criminology, and linguistics, the study captures perspectives that are rarely integrated within a single analytical framework. Using NVivo 14 and inductive thematic analysis, six core themes and seven sub-themes were identified, revealing previously underexplored cognitive-emotional, social-relational, and platform-mediated mechanisms of victimisation. The key contribution of this research is not the identification of entirely new susceptibility factors, but the development of an interdisciplinary framework that integrates these previously disconnected dimensions. By foregrounding the role of SNS design affordances within the broader Internet ecosystem and their interaction with human cognition and social dynamics, this study advances current understanding beyond fragmented models of user vulnerability. The findings provide a novel conceptual foundation for future empirical research and inform the design of more effective, context-aware mitigation and awareness strategies for SECAs on Internet-based SNSs. Full article

(This article belongs to the Special Issue Adversarial Attacks and Cyber Security)

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23 pages, 1402 KB

Open AccessArticle

A Deception-Based Access Control Mechanism for Protecting PLCs from ModbusTCP Brute-Force Attacks in IIoT Environments

by Mohammad AbdulJawad, Mohammad Z. Masoud, Álvaro Álesanco and José García

Future Internet 2026, 18(5), 259; https://doi.org/10.3390/fi18050259 - 14 May 2026

Viewed by 548

Abstract

Industrial control systems (ICSs) increasingly rely on legacy communication protocols such as ModbusTCP, which lack built-in security mechanisms and remain widely exposed to network-based attacks. This paper investigates the security limitations of authentication mechanisms in ModbusTCP-enabled programmable logic controllers (PLCs) and demonstrates how plaintext credential transmission and limited connection handling capabilities can be exploited to perform brute-force and denial-of-service (DoS) attacks. An experimental testbed based on two industrial Delta PLC families (DVP-13SE and DVP-311SV3) was developed to systematically evaluate these vulnerabilities under realistic conditions. The results show that authentication credentials can be easily captured through network sniffing, while the PLC communication stack supports a maximum of 16 concurrent connections and can process up to approximately 8600 Modbus operations per second, making it susceptible to resource exhaustion and performance degradation under distributed attack scenarios. To address these limitations, this paper proposes a lightweight deception-based protection mechanism, termed the PLC misleading algorithm (PMA), which is implemented directly within the PLC ladder logic. Unlike traditional network-level defenses, PMA operates at the device level and dynamically misleads attackers by generating controlled randomized responses while preserving consistent behavior for legitimate clients. Experimental results demonstrate that PMA significantly mitigates brute-force effectiveness by preventing reliable password extraction while introducing minimal overhead (2.2% memory usage) and maintaining acceptable communication latency. Additionally, the proposed approach significantly reduces observable attack traffic, with only 0.246 Modbus operations per second observed during the attack phase, thereby limiting the effectiveness of automated exploitation tools. These findings highlight the potential of in-device deception mechanisms as a practical and deployable security layer for legacy industrial systems, and provide new insights into the resilience of PLC-based infrastructures against network-level attacks. This work bridges the gap between lightweight PLC-level protections and the growing need for robust cybersecurity mechanisms in industrial IoT environments. Full article

(This article belongs to the Special Issue Adversarial Attacks and Cyber Security)

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26 pages, 2242 KB

Open AccessArticle

A Multi-Source Feedback-Driven Framework for Generating WAF Test Cases

by Pengcheng Lu, Xiaofeng Zhong, Wenbo Xu and Yongjie Wang

Future Internet 2026, 18(3), 167; https://doi.org/10.3390/fi18030167 - 20 Mar 2026

Viewed by 925

Abstract

Web application firewalls (WAFs) are critical defenses against persistent threats to web applications, yet their security evaluation remains challenging. Traditional manual testing methods are often inefficient and resource-intensive, while existing reinforcement learning (RL)-based automated approaches face two key limitations: (1) attackers cannot perceive opaque WAF rule logic; (2) boolean feedback from WAFs results in sparse/delayed rewards—sparse rewards trap agents in blind exploration, and delayed rewards hinder the association between early actions and final outcomes, adversely affecting learning efficiency. To address those challenges, we propose Ouroboros—a framework integrating genetic algorithm-based symbolic rule reconstruction (translating WAF rules into interpretable RNNs for fine-grained confidence scoring), timing side-channel analysis (evaluating rule-matching depth), and a multi-tiered reward mechanism to enable self-evolving RL testing. Experiments show that the framework reaches 89.2% bypass success rate on signature-based WAFs. This paper presents an efficient solution for automated WAF testing and delivers insights for optimizing rule logic and anomaly detection mechanisms. Full article

(This article belongs to the Special Issue Adversarial Attacks and Cyber Security)

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23 pages, 968 KB

Open AccessArticle

TLOA: A Power-Adaptive Algorithm Based on Air–Ground Cooperative Jamming

by Wenpeng Wu, Zhenhua Wei, Haiyang You, Zhaoguang Zhang, Chenxi Li, Jianwei Zhan and Shan Zhao

Future Internet 2026, 18(2), 81; https://doi.org/10.3390/fi18020081 - 2 Feb 2026

Cited by 1 | Viewed by 571

Abstract

Air–ground joint jamming enables three-dimensional, distributed jamming configurations, making it effective against air–ground communication networks with complex, dynamically adjustable links. Once the jamming layout is fixed, dynamic jamming power scheduling becomes essential to conserve energy and prolong jamming duration. However, existing methods suffer from poor applicability in such scenarios, primarily due to their sparse deployment and adversarial nature. To address this limitation, this paper develops a set of mathematical models and a dedicated algorithm for air–ground communication countermeasures. Specifically, we (1) randomly select communication nodes to determine the jammer operation sequence; (2) schedule the number of active jammers by sorting transmission path losses in ascending order; and (3) estimate jamming effects using electromagnetic wave propagation characteristics to adjust jamming power dynamically. This approach formally converts the original dynamic, stochastic jamming resource scheduling problem into a static, deterministic one via cognitive certainty of dynamic parameters and deterministic modeling of stochastic factors—enabling rapid adaptation to unknown, dynamic communication power strategies and resolving the coordination challenge in air–ground joint jamming. Experimental results demonstrate that the proposed Transmission Loss Ordering Algorithm (TLOA) extends the system operating duration by up to 41.6% compared to benchmark methods (e.g., genetic algorithm). Full article

(This article belongs to the Special Issue Adversarial Attacks and Cyber Security)

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14 pages, 508 KB

Open AccessArticle

Cross-Gen: An Efficient Generator Network for Adversarial Attacks on Cross-Modal Hashing Retrieval

by Chao Hu, Li Chen, Sisheng Li, Yin Yi, Yu Zhan, Chengguang Liu, Jianling Liu and Ronghua Shi

Future Internet 2025, 17(12), 573; https://doi.org/10.3390/fi17120573 - 13 Dec 2025

Cited by 1 | Viewed by 606

Abstract

Research on deep neural network (DNN)-based multi-dimensional data visualization has thoroughly explored cross-modal hash retrieval (CMHR) systems, yet their vulnerability to malicious adversarial examples remains evident. Recent work improves the robustness of CMHR networks by augmenting training datasets with adversarial examples. Prior approaches typically formulate the generation of cross-modal adversarial examples as an optimization problem solved through iterative methods. Although effective, such techniques often suffer from slow generation speed, limiting research efficiency. To address this, we propose a generative-based method that enables rapid synthesis of adversarial examples via a carefully designed adversarial generator network. Specifically, we introduce Cross-Gen, a parallel cross-modal framework that constructs semantic triplet data by interacting with the target model through query-based feedback. The generator is optimized using a tailored objective comprising adversarial loss, reconstruction loss, and quantization loss. The experimental results show that Cross-Gen generates adversarial examples significantly faster than iterative methods while achieving competitive attack performance. Full article

(This article belongs to the Special Issue Adversarial Attacks and Cyber Security)

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48 pages, 10659 KB

Open AccessArticle

Evaluating Synthetic Malicious Network Traffic Generated by GAN and VAE Models: A Data Quality Perspective

by Nikolaos Peppes, Theodoros Alexakis, Emmanouil Daskalakis and Evgenia Adamopoulou

Future Internet 2025, 17(12), 561; https://doi.org/10.3390/fi17120561 - 4 Dec 2025

Cited by 4 | Viewed by 1932

Abstract

The limited availability and imbalance of labeled malicious network traffic data remain major obstacles in developing effective AI-driven cybersecurity solutions. To mitigate these challenges, this study investigates the use of deep generative models, specifically Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), for producing realistic synthetic attack data. A comprehensive data quality assessment (DQA) framework is proposed to thoroughly evaluate the fidelity, diversity, and practical utility of the generated data samples. The findings support the adoption of data synthesis as a viable strategy to address data scarcity, improving robustness and reliability in modern cybersecurity applications and sectors. Full article

(This article belongs to the Special Issue Adversarial Attacks and Cyber Security)

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27 pages, 1438 KB

Open AccessArticle

Towards Proactive Domain Name Security: An Adaptive System for .ro domains Reputation Analysis

by Carmen Ionela Rotună, Ioan Ștefan Sacală and Adriana Alexandru

Future Internet 2025, 17(10), 478; https://doi.org/10.3390/fi17100478 - 18 Oct 2025

Cited by 1 | Viewed by 2308

Abstract

In a digital landscape marked by the exponential growth of cyber threats, the development of automated domain reputation systems is extremely important. Emerging technologies such as artificial intelligence and machine learning now enable proactive and scalable approaches to early identification of malicious or suspicious domains. This paper presents an adaptive domain name reputation system that integrates advanced machine learning to enhance cybersecurity resilience. The proposed framework uses domain data from .ro domain Registry and several other sources (blacklists, whitelists, DNS, SSL certificate), detects anomalies using machine learning techniques, and scores domain security risk levels. A supervised XGBoost model is trained and assessed through five-fold stratified cross-validation and a held-out 80/20 split. On an example dataset of 25,000 domains, the system attains accuracy 0.993 and F1 0.993 and is exposed through a lightweight Flask service that performs asynchronous feature collection for near real-time scoring. The contribution is a blueprint that links list supervision with registry/DNS/TLS features and deployable inference to support proactive domain abuse mitigation in ccTLD environments. Full article

(This article belongs to the Special Issue Adversarial Attacks and Cyber Security)

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