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Table of Content

Cyberspace Intelligent Mapping and Situational Awareness

Submission Deadline: 25 December 2022 (closed)

Guest Editors

Prof. Xiangyang Luo, State Key Laboratory of Mathematical Engineering and Advanced Computing, China; Key Laboratory of Cyberspace Situation Awareness of Henan Province, China
Prof. Yun-Qing Shi, New Jersey Institute of Technology, USA
Prof. Jinwei Wang, Nanjing University of Information Science & Technology, China
Prof. Qi Liu, Edinburgh Napier University, UK
Prof. Le Sun, Sungkyunkwan University, Korea
Dr. Yi Zhang, State Key Laboratory of Mathematical Engineering and Advanced Computing, China; Key Laboratory of Cyberspace Situation Awareness of Henan Province, China

Summary

As a new confrontation field, cyberspace needs a comprehensive and accurate cyberspace map oriented to cybergovernance confrontation actions. Cyberspace intelligent mapping is a technology for constructing cyberspace maps. The goal is to detect, measure, analyze and draw network resources of various sources and types. Furthermore, network security situational awareness acquires, understands, evaluates and predicts future development trends of many elements that affect network security. It is a means of quantitative analysis of network security and a fine measure of network security. The focus of security technology in the era of network security 2.0 plays a very important role in ensuring network security.


This special issue focuses on cyberspace intelligent mapping and situational awareness. We invite researchers to contribute original research articles as well as review articles to this special issue. Potential topics include, but are not limited to:

 

New models and methods in cyberspace mapping

Network intelligent mapping algorithms

Cyberspace data collection and analysis

Multimedia encryption and forensic

Covert communication for new network scenarios

Artificial intelligence based communication carrier generation

Privacy protection and digital forensics

Cyberspace situational understanding

Machine learning, data mining, and information retrieval for network relationship understanding

Network situational assessment and prediction

Visualization and interaction method of cyberspace situation

Possible threat judgment and early warning in cyberspace


Keywords

Cyberspace mapping; Situational awareness; Cyberspace data collection and analysis; Cyberspace situational understanding; Network possible threat warning.

Published Papers


  • Open Access

    ARTICLE

    Identifying Industrial Control Equipment Based on Rule Matching and Machine Learning

    Yuhao Wang, Yuying Li, Yanbin Sun, Yu Jiang
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 577-605, 2023, DOI:10.32604/cmes.2023.026791
    (This article belongs to this Special Issue: Cyberspace Intelligent Mapping and Situational Awareness)
    Abstract To identify industrial control equipment is often a key step in network mapping, categorizing network resources, and attack defense. For example, if vulnerable equipment or devices can be discovered in advance and the attack path can be cut off, security threats can be effectively avoided and the stable operation of the Internet can be ensured. The existing rule-matching method for equipment identification has limitations such as relying on experience and low scalability. This paper proposes an industrial control device identification method based on PCA-Adaboost, which integrates rule matching and machine learning. We first build a rule base from network data… More >

  • Open Access

    ARTICLE

    Adaptive Backdoor Attack against Deep Neural Networks

    Honglu He, Zhiying Zhu, Xinpeng Zhang
    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2617-2633, 2023, DOI:10.32604/cmes.2023.025923
    (This article belongs to this Special Issue: Cyberspace Intelligent Mapping and Situational Awareness)
    Abstract In recent years, the number of parameters of deep neural networks (DNNs) has been increasing rapidly. The training of DNNs is typically computation-intensive. As a result, many users leverage cloud computing and outsource their training procedures. Outsourcing computation results in a potential risk called backdoor attack, in which a welltrained DNN would perform abnormally on inputs with a certain trigger. Backdoor attacks can also be classified as attacks that exploit fake images. However, most backdoor attacks design a uniform trigger for all images, which can be easily detected and removed. In this paper, we propose a novel adaptive backdoor attack.… More >

  • Open Access

    ARTICLE

    GraphCWGAN-GP: A Novel Data Augmenting Approach for Imbalanced Encrypted Traffic Classification

    Jiangtao Zhai, Peng Lin, Yongfu Cui, Lilong Xu, Ming Liu
    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 2069-2092, 2023, DOI:10.32604/cmes.2023.023764
    (This article belongs to this Special Issue: Cyberspace Intelligent Mapping and Situational Awareness)
    Abstract Encrypted traffic classification has become a hot issue in network security research. The class imbalance problem of traffic samples often causes the deterioration of Machine Learning based classifier performance. Although the Generative Adversarial Network (GAN) method can generate new samples by learning the feature distribution of the original samples, it is confronted with the problems of unstable training and mode collapse. To this end, a novel data augmenting approach called GraphCWGAN-GP is proposed in this paper. The traffic data is first converted into grayscale images as the input for the proposed model. Then, the minority class data is augmented with… More >

  • Open Access

    ARTICLE

    Secure Downlink Transmission Strategies against Active Eavesdropping in NOMA Systems: A Zero-Sum Game Approach

    Yanqiu Chen, Xiaopeng Ji
    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 531-553, 2023, DOI:10.32604/cmes.2023.024531
    (This article belongs to this Special Issue: Cyberspace Intelligent Mapping and Situational Awareness)
    Abstract Non-orthogonal multiple access technology (NOMA), as a potentially promising technology in the 5G/B5G era, suffers from ubiquitous security threats due to the broadcast nature of the wireless medium. In this paper, we focus on artificial-signal-assisted and relay-assisted secure downlink transmission schemes against external eavesdropping in the context of physical layer security, respectively. To characterize the non-cooperative confrontation around the secrecy rate between the legitimate communication party and the eavesdropper, their interactions are modeled as a two-person zero-sum game. The existence of the Nash equilibrium of the proposed game models is proved, and the pure strategy Nash equilibrium and mixed-strategy Nash… More >

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