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Cyberspace Security Technology in Computer Science
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Cyberspace Security Technology in Computer Science

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 November 2025 | Viewed by 1378

Special Issue Editors

Dr. Cheng Huang

E-Mail Website
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

E-Mail Website
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 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. 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 (2 papers)

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Research

19 pages, 599 KiB  
Article
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 372
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 [...] Read more.
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 KiB  
Article
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
Viewed by 402
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 [...] Read more.
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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