Special Issues
Table of Content

Cyberspace Mapping and Anti-Mapping Techniques

Submission Deadline: 31 March 2026 View: 810 Submit to Special Issue

Guest Editors

Prof. Xiangyang Luo

Email: luoxy_ieu@sina.com

Affiliation: State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, 450000, China; Key Laboratory of Cyberspace Situation Awareness of Henan Province, Zhengzhou, 450000, China

Homepage:

Research Interests: cyberspace (anti-) mapping, IP geolocation

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Prof. Xiangyang Xue

Email: xyxue@fudan.edu.cn

Affiliation: School of Computer Science, Fudan University, Shanghai, 200000, China

Homepage:

Research Interests: big data analysis, machine learning for networks

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Prof. Guopu Zhu

Email: guopu.zhu@hit.edu.cn

Affiliation: School of Cyberspace Science,Harbin Institute of Technology, Harbin, 150000, China

Homepage:

Research Interests: cyberspace situation awareness, Information content security

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Dr. Meng Zhang

Email: zhangmeng_ieu@sina.com

Affiliation: State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, 450000, China; Key Laboratory of Cyberspace Situation Awareness of Henan Province, Zhengzhou, 450000, China

Homepage:

Research Interests: user attribute perception, social network analysis

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Summary

Cyberspace Mapping and Anti-Mapping Technologies have emerged as core research directions in the field of cybersecurity, addressing the complexity and stealthiness of cyber threats in the digital era.

The objective of cyberspace mapping is to systematically identify the network attributes, geographical attributes, and social attributes of virtual/physical resources within target networks, alongside their interdependencies. Conversely, anti-mapping technologies focus on evading or mitigating unauthorized reconnaissance activities to safeguard critical network resources from exposure and exploitation. This special issue concentrates on theoretical advancements, technical innovations, and practical applications in cyberspace mapping and anti-mapping technologies. It covers domains such as network topology analysis, (anti-)scanning detection and identification techniques, (anti-)critical node and path analysis methods, cross-domain correlation mapping, and more. These contributions aim to provide technical support for the protection of critical infrastructure and global cyberspace governance.

The following themes are the particular interest of this special issue, including but not limited to:
- Detection and anti-detection technologies for cyberspace physical resources
- Network device geolocation and anti-geolocation methods
- Cyberspace asset identification and dynamic modelling techniques
- Cyberspace data collection and multidimensional analysis
- Perception and anti-perception strategies for social attributes of network users
- Deepfake generation and forgery authentication technologies


Keywords

cyberspace mapping and anti-mapping, cyberspace resource (anti-)detection, network device (anti-)geolocation, cyber asset identification, cross-domain correlation mapping, deepfake generation, forgery authentication

Published Papers


  • Open Access

    ARTICLE

    Defending against Topological Information Probing for Online Decentralized Web Services

    Xinli Hao, Qingyuan Gong, Yang Chen
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.073155
    (This article belongs to the Special Issue: Cyberspace Mapping and Anti-Mapping Techniques)
    Abstract Topological information is very important for understanding different types of online web services, in particular, for online social networks (OSNs). People leverage such information for various applications, such as social relationship modeling, community detection, user profiling, and user behavior prediction. However, the leak of such information will also pose severe challenges for user privacy preserving due to its usefulness in characterizing users. Large-scale web crawling-based information probing is a representative way for obtaining topological information of online web services. In this paper, we explore how to defend against topological information probing for online web services,… More >

  • Open Access

    REVIEW

    From Identification to Obfuscation: A Survey of Cross-Network Mapping and Anti-Mapping Methods

    Shaojie Min, Yaxiao Luo, Kebing Liu, Qingyuan Gong, Yang Chen
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.073175
    (This article belongs to the Special Issue: Cyberspace Mapping and Anti-Mapping Techniques)
    Abstract User identity linkage (UIL) across online social networks seeks to match accounts belonging to the same real-world individual. This cross-platform mapping enables accurate user modeling but also raises serious privacy risks. Over the past decade, the research community has developed a wide range of UIL methods, from structural embeddings to multimodal fusion architectures. However, corresponding adversarial and defensive approaches remain fragmented and comparatively understudied. In this survey, we provide a unified overview of both mapping and anti-mapping methods for UIL. We categorize representative mapping models by learning paradigm and data modality, and systematically compare them… More >

  • Open Access

    ARTICLE

    A Virtual Probe Deployment Method Based on User Behavioral Feature Analysis

    Bing Zhang, Wenqi Shi
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.067470
    (This article belongs to the Special Issue: Cyberspace Mapping and Anti-Mapping Techniques)
    Abstract To address the challenge of low survival rates and limited data collection efficiency in current virtual probe deployments, which results from anomaly detection mechanisms in location-based service (LBS) applications, this paper proposes a novel virtual probe deployment method based on user behavioral feature analysis. The core idea is to circumvent LBS anomaly detection by mimicking real-user behavior patterns. First, we design an automated data extraction algorithm that recognizes graphical user interface (GUI) elements to collect spatio-temporal behavior data. Then, by analyzing the automatically collected user data, we identify normal users’ spatio-temporal patterns and extract their… More >

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