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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 August 2026 | Viewed by 7292

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Special Issue Editors

Dr. Cheng Huang

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Guest Editor

School of Cyber Science and Engineering, Sichuan University, Chengdu 610065, China
Interests: data-driven security; network security; threat intelligence
Special Issues, Collections and Topics in MDPI journals

Dr. Xiaoqi Li

E-Mail Website
Guest Editor

School of Cyberspace Security, Hainan University, Haikou 570228, China
Interests: blockchain; computer security; software engineering; artificial intelligence

Prof. Dr. Le Yu

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Guest Editor

School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Interests: system security; software engineering

Dr. Jiachi Chen

E-Mail Website
Guest Editor

School of Software Engineering, Sun Yat-sen University, Guangzhou 510275, China
Interests: smart contracts; blockchain; large language models; software engineering

Special Issue Information

Dear Colleagues,

This Special Issue seeks to solicit high-quality original research articles and comprehensive reviews that address fundamental and applied aspects of cyberspace security technology within the scope of computer science. Contributions that propose novel theoretical models, practical solutions, and interdisciplinary approaches are particularly encouraged. The scope of this Special Issue encompasses, but is not limited to, the following topics:

Advanced threat detection and response systems;
Cybersecurity frameworks leveraging AI;
Blockchain technology for enhanced security and privacy;
Cybersecurity policy and compliance in digital environments;
Vulnerability assessment and penetration testing methodologies;
Incident response and recovery strategies in complex networks;
Secure software development practices and code analysis;
Cyber threat intelligence sharing and collaboration;
Privacy-preserving technologies and data protection measures;
Human factors in cybersecurity and user behavior analysis.

Dr. Cheng Huang
Dr. Xiaoqi Li
Prof. Dr. Le Yu
Dr. Jiachi Chen
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. Applied Sciences 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

cyberspace security technology
cyber threat detection
artificial intelligence in security
blockchain security
vulnerability assessment
incident response
privacy protection
human factors in cybersecurity

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Published Papers (6 papers)

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Research

35 pages, 1374 KB

Open AccessArticle

Stream Encryption Cryptographic Systems Based on Asymmetric Cet Operations with an Accuracy of Permutation

by Serhii Semenov, Volodymyr Rudnytskyi, Nаtaliia Lada, Volodymyr Krivtsun, Tymofii Korotkyi, Vitalii Zazhoma and Olga Wasiuta

Appl. Sci. 2026, 16(10), 4987; https://doi.org/10.3390/app16104987 - 16 May 2026

Viewed by 77

Abstract

This paper addresses the problem of constructing adaptive stream encryption transformations based on dynamically generated Boolean mappings. A formal framework for modeling and modifying asymmetric two-operand Conditional Elementary Transformations (CETs) is proposed, where new operations are obtained through permutation-driven modification of operands and transformation results. The main contribution of the study is the development of a method for generating groups of CET-based transformations and corresponding generator models that enable the construction of pseudorandom sequences of dynamically varying substitution rules. The proposed approach ensures preservation of bijectivity and establishes formal relationships between direct and inverse operations under transformation modifications. Experimental evaluation demonstrates that the generated CET-based transformations produce output sequences with entropy close to the theoretical maximum (H ≈ 1) while providing enhanced diffusion properties. In particular, the CET_base configuration achieves an avalanche effect of approximately 0.79 compared to ≈0.5 for the classical XOR baseline. At the same time, permutation-based variants introduce additional structural diversity, enabling flexible trade-offs between diffusion strength and variability of transformation behavior. The obtained results confirm that the proposed framework enables systematic construction of large families of Boolean mappings, including up to 16 and 64 S-box transformations for 2Ci- and 3Ci-quanta operations, respectively, exceeding the capabilities of fixed XOR-based schemes. The proposed approach is intended as a flexible design paradigm for adaptive and lightweight cryptographic systems. However, the current study is limited to structural and statistical analysis, and a formal evaluation of resistance to established cryptanalytic attacks remains a subject of future research. Full article

(This article belongs to the Special Issue Cyberspace Security Technology in Computer Science)

23 pages, 1208 KB

Open AccessArticle

NeSySwarm-IDS: End-to-End Differentiable Neuro-Symbolic Logic for Privacy-Preserving Intrusion Detection in UAV Swarms

by Gang Yang, Lin Ni, Tao Xia, Qinfang Shi and Jiajian Li

Appl. Sci. 2026, 16(7), 3204; https://doi.org/10.3390/app16073204 - 26 Mar 2026

Viewed by 606

Abstract

Unmanned Aerial Vehicle (UAV) swarms operating in contested environments face a critical “semantic gap” between raw, high-velocity network traffic and high-level mission security constraints, compounded by the risk of privacy leakage during collaborative learning. Existing deep learning (DL)-based Network Intrusion Detection Systems (NIDSs) suffer from opacity, prohibitive resource consumption, and vulnerability to gradient leakage attacks in federated settings, while traditional rule-based systems fail to handle encrypted payloads and evolving attack patterns. To bridge this gap, we present NeSySwarm-IDS (Neuro-Symbolic Swarm Intrusion Detection System), an end-to-end differentiable neuro-symbolic framework that simultaneously achieves high accuracy, strong privacy guarantees, and built-in interpretability under resource constraints. NeSySwarm-IDS integrates an extremely lightweight 1D convolutional neural network with a differentiable Łukasiewicz fuzzy logic reasoner incorporating attack-specific rules. By aggregating only low-dimensional logic rule weights with calibrated differential privacy noise, we drastically reduce communication overhead while providing

(ϵ, δ)

-DP guarantees with negligible utility loss. Extensive experiments on the UAV-NIDD dataset and our self-collected dataset demonstrate that NeSySwarm-IDS achieves near-perfect detection accuracy, significantly outperforming traditional machine learning baselines despite using limited training data. A detailed case study on GPS spoofing confirms the interpretability of our approach, providing axiomatic explanations suitable for autonomous mission verification. These results establish that end-to-end neuro-symbolic learning can effectively bridge the semantic gap in UAV swarm security while ensuring privacy and interpretability, offering a practical pathway for deploying trustworthy AI in contested environments. Full article

(This article belongs to the Special Issue Cyberspace Security Technology in Computer Science)

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19 pages, 807 KB

Open AccessArticle

DAG-Guided Active Fuzzing: A Deterministic Approach to Detecting Race Conditions in Distributed Cloud Systems

by Hongyi Zhao, Zhen Li, Yueming Wu and Deqing Zou

Appl. Sci. 2026, 16(4), 2061; https://doi.org/10.3390/app16042061 - 19 Feb 2026

Viewed by 647

Abstract

The rapid expansion of distributed cloud platforms introduces critical security challenges, specifically non-deterministic race conditions like Time-of-Check to Time-of-Use (TOCTOU) vulnerabilities. Traditional passive detection methods often fail to identify these transient “Heisenbugs” due to the asynchronous nature of multi-threaded control planes. To address this, we propose a novel DAG-Guided Active Fuzzing framework. Our approach constructs a Directed Acyclic Graph (DAG) to map causal dependencies of API operations and implements deterministic proactive scheduling. By injecting microsecond-level delays into identified race windows, the system enforces adversarial interleavings to expose hidden order and atomicity violations. Validated on 32 verified vulnerabilities across six distributed systems (including Hadoop and OpenStack), our method achieves an overall Recall (Detection Rate) of 68.8% across the entire dataset and a peak Precision of 92% in reproducibility tests, significantly outperforming random fuzzing baselines (

p < 0.01

). Furthermore, the framework maintains a low runtime overhead of 11.5%. These findings demonstrate a favorable trade-off between detection depth and system efficiency, establishing the approach as a robust toolchain for transforming theoretical concurrency risks into reproducible security findings in large-scale cloud infrastructure. Full article

(This article belongs to the Special Issue Cyberspace Security Technology in Computer Science)

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16 pages, 2422 KB

Open AccessArticle

Enhancing Binary Security Analysis Through Pre-Trained Semantic and Structural Feature Matching

by Chen Yi, Wei Dai, Yiqi Deng, Liang Bao and Guoai Xu

Appl. Sci. 2025, 15(21), 11610; https://doi.org/10.3390/app152111610 - 30 Oct 2025

Viewed by 1435

Abstract

Binary code similarity detection serves as a critical front-line defense mechanism in cybersecurity, playing an indispensable role in identifying known vulnerabilities, detecting emergent malware families, and preventing intellectual property theft via code plagiarism. However, existing methods based on Control Flow Graphs (CFGs) often suffer from two major limitations: the inadequate capture of deep semantic information within CFG nodes, and the neglect of structural relationships across different functions. To address these issues, we propose Breg, a novel framework that synergistically integrates pre-trained semantic features with cross-graph structural features. Breg employs a BERT model pre-trained on a large-scale binary corpus to capture nuanced semantic relationships, and introduces a Cross-Graph Neural Network (CGNN) to explicitly model topological correlations between two CFGs, thereby generating highly discriminative embeddings. Extensive experimental validation demonstrates that Breg achieves leading F1-scores of 0.8682 and 0.8970 on Dataset3. In real-world vulnerability search tasks on Dataset4, Breg achieves an MRR@10 of 0.9333 in the challenging MIPS32-to-x64 search task, a clear improvement over the 0.8533 scored by the strongest baseline. This underscores its superior effectiveness and robustness across diverse compilation environments and architectures. To the best of our knowledge, this is the first work to integrate a pre-trained language model with cross-graph structural learning for binary code similarity detection, offering enhanced effectiveness, generalization, and practical applicability in real-world security scenarios. Full article

(This article belongs to the Special Issue Cyberspace Security Technology in Computer Science)

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19 pages, 599 KB

Open AccessArticle

Effective Seed Scheduling for Directed Fuzzing with Function Call Sequence Complexity Estimation

by Xi Peng, Peng Jia, Ximing Fan, Cheng Huang and Jiayong Liu

Appl. Sci. 2025, 15(15), 8345; https://doi.org/10.3390/app15158345 - 26 Jul 2025

Viewed by 1455

Abstract

Directed grey-box fuzzers focus on testing specific target code. They have been utilized in various security applications, such as reproducing known crashes and identifying vulnerabilities resulting from incomplete patches. Distance-guided directed fuzzers calculate the distance to the target node for each node in a CFG or CG, which has always been the mainstream in this field. However, the distance can only reflect the relationship between the current node and the target node, and it does not consider the impact of the reaching sequence before the target node. To mitigate this problem, we analyzed the properties of the instrumented function’s call graph after selective instrumentation, and the complexity of reaching the target function sequence was estimated. Assisted by the sequence complexity, we proposed a two-stage function call sequence-based seed-scheduling strategy. The first stage is to select seeds with a higher probability of generating test cases that reach the target function. The second stage is to select seeds that can generate test cases that meet the conditions for triggering the vulnerability as much as possible. We implemented our approach in SEZZ based on SelectFuzz and compare it with related works. We found that SEZZ outperformed AFLGo, Beacon, WindRanger, and SelectFuzz by achieving an average improvement of 13.7×, 1.50×, 9.78×, and 2.04× faster on vulnerability exposure, respectively. Moreover, SEZZ triggered three more vulnerabilities than the other compared tools. Full article

(This article belongs to the Special Issue Cyberspace Security Technology in Computer Science)

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21 pages, 1632 KB

Open AccessArticle

Adversarial Hierarchical-Aware Edge Attention Learning Method for Network Intrusion Detection

by Hao Yan, Jianming Li, Lei Du, Binxing Fang, Yan Jia and Zhaoquan Gu

Appl. Sci. 2025, 15(14), 7915; https://doi.org/10.3390/app15147915 - 16 Jul 2025

Cited by 1 | Viewed by 1759

Abstract

The rapid development of information technology has made cyberspace security an increasingly critical issue. Network intrusion detection methods are practical approaches to protecting network systems from cyber attacks. However, cyberspace security threats have topological dependencies and fine-grained attack semantics. Existing graph-based approaches either underestimate edge-level features or fail to balance detection accuracy with adversarial robustness. To handle these problems, we propose a novel graph neural network–based method for network intrusion detection called the adversarial hierarchical-aware edge attention learning method (AH-EAT). It leverages the natural graph structure of computer networks to achieve robust, multi-grained intrusion detection. Specifically, AH-EAT includes three main modules: an edge-based graph attention embedding module, a hierarchical multi-grained detection module, and an adversarial training module. In the first module, we apply graph attention networks to aggregate node and edge features according to their importance. This effectively captures the network’s key topological information. In the second module, we first perform coarse-grained detection to distinguish malicious flows from benign ones, and then perform fine-grained classification to identify specific attack types. In the third module, we use projected gradient descent to generate adversarial perturbations on network flow features during training, enhancing the model’s robustness to evasion attacks. Experimental results on four benchmark intrusion detection datasets show that AH-EAT achieves 90.73% average coarse-grained accuracy and 1.45% ASR on CIC-IDS2018 under adversarial attacks, outperforming state-of-the-art methods in both detection accuracy and robustness. Full article

(This article belongs to the Special Issue Cyberspace Security Technology in Computer Science)

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