Intelligent Data Analysis in Cyberspace

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 6763

Special Issue Editors

Key Laboratory of Cyberculture Content Cognition and Detection, Ministry of Culture and Tourism, University of Science and Technology of China, Hefei 230026, China
Interests: social bots; cyber security; social networks
Special Issues, Collections and Topics in MDPI journals
Department of Cyber Security, University of Science and Technology of China, Hefei 230026, China
Interests: federated learning; edge AI; XR

Special Issue Information

Dear Colleagues,

The development of data science and the rise of computing intelligence technologies have provided opportunities for applying big data in various fields and industries. Cyberspace maps the real physical world to the virtual digital world. It is the sum of all interconnected information systems. With the interconnection and fusion of these systems, cyberspace has entered diverse fields, such as politics, economy, military, technology, and culture. Big data and cyberspace are becoming increasingly inseparable. Cyberspace forms the infrastructure for big data collection, storage, analysis, and knowledge creation. Big data is a vital part of cyberspace and a tool for analyzing and understanding various individuals, groups, events, content, and behaviors.

This Special Issue solicits papers on new research achievements and challenges in intelligent data analysis in cyberspace.

Topics of interest in this Special Issue include but are not limited to the following:

  • Information Retrieval for Cyberspace
  • Data Model and Structure for Big Data
  • Machine Learning Theory for Big Data
  • Modeling Cyberspace and Behaviour
  • Big Data Analytics and Processing
  • Big Data Representation and Visualization
  • Architectures and Designs of Big Data Processing Systems
  • Recommender Systems Applications in Cyberspace
  • Social Bot Detection and Behaviour Analysis in Cyberspace
  • Knowledge Graph in Cyberspace

Dr. Yangyang Li 
Dr. Pengyuan Zhou
Guest Editors

Manuscript Submission Information

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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. Electronics 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

  • big data 
  • cyberspace 
  • machine learning 
  • social network 
  • knowledge graph 
  • recommender system 
  • visualization

Published Papers (5 papers)

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Research

18 pages, 3761 KiB  
Article
Research on the Classification Methods of Social Bots
Electronics 2023, 12(14), 3030; https://doi.org/10.3390/electronics12143030 - 10 Jul 2023
Cited by 1 | Viewed by 699
Abstract
In order to ensure the healthy development of social networks and the harmony and stability of the society, as well as to facilitate effective supervision by regulatory authorities, a classification method of social bots is proposed based on the identification of social bots [...] Read more.
In order to ensure the healthy development of social networks and the harmony and stability of the society, as well as to facilitate effective supervision by regulatory authorities, a classification method of social bots is proposed based on the identification of social bots in the early stage. First of all, the topic-related introduction is used to expand the topic, and on this basis, the SBERT (Sentence-BERT) model is applied to make relevance judgments between the micro-blog text and the expanded topics to identify polluters. Then, an opinion sentence recognition method that combines social bots opinion sentence generation rules with a deep learning model TextCNN is proposed to further distinguish commenters and spreaders. Finally, in order to improve the classification effect of the model, the transfer learning method is used to train the model with the help of a large number of micro-blogs of ordinary Weibo accounts, so as to better improve the classification effect of social bots. The comparative experimental results show that the topic expansion method can effectively improve the classification results of the SBERT model for the relevance of micro-blog text topics. When the parameter k of the expanded topic model is set at 20, the content of the expanded topic sequence is more consistent with the core content of most Weibo text sequences, and the obtained model has the best performance. By analyzing the opinion-based micro-blog text generation rules of social bots, focusing on the keywords that express opinions, the problem of difficulty in recognizing opinion sentences produced by the low quality of opinion sentences of social bots is well resolved, and the recognition effect of opinion sentences has been improved by more than 10%. Through the introduction of transfer learning, the problem of insufficient social bots data is effectively alleviated, and the classification effect of social bots is greatly improved, with an increase of more than 10%. Full article
(This article belongs to the Special Issue Intelligent Data Analysis in Cyberspace)
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14 pages, 592 KiB  
Article
A Cybersecurity Knowledge Graph Completion Method for Penetration Testing
Electronics 2023, 12(8), 1837; https://doi.org/10.3390/electronics12081837 - 12 Apr 2023
Viewed by 1392
Abstract
Penetration testing is an effective method of making computers secure. When conducting penetration testing, it is necessary to fully understand the various elements in the cyberspace. Prediction of future cyberspace state through perception and understanding of cyberspace can assist defenders in decision-making and [...] Read more.
Penetration testing is an effective method of making computers secure. When conducting penetration testing, it is necessary to fully understand the various elements in the cyberspace. Prediction of future cyberspace state through perception and understanding of cyberspace can assist defenders in decision-making and action execution. Accurate cyberspace detection information is the key to ensuring successful penetration testing. However, cyberspace situation awareness still faces the following challenges. Due to the limited detection capability, the information obtained from cyberspace detection intelligence is incomplete. There are some errors in the cyberspace detection intelligence, which may mislead the penetration testing workers. The knowledge graph can store and manage the cybersecurity data. In order to ensure the integrity and accuracy of cyberspace information, we design a knowledge graph completion model called CSNT to complete cybersecurity data. CSNT uses the BiLSTM to capture the interaction information between entities and relationships. It models the relationship between entities by combining the neural network and tensor decomposition. The Pearson Mix Net is designed to control the generation of joint vectors. We also design a novel self-distillation strategy to reduce catastrophic forgetting during model training. After learning the relationship pattern between entities in the cyberspace detection intelligence, the model can be used to mine the knowledge not found in the cybersecurity detection intelligence and correct the erroneous records. Experiments show that our method has certain advantages for the knowledge graph completion. Full article
(This article belongs to the Special Issue Intelligent Data Analysis in Cyberspace)
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16 pages, 1312 KiB  
Article
A Dynamic Short Cascade Diffusion Prediction Network Based on Meta-Learning-Transformer
Electronics 2023, 12(4), 837; https://doi.org/10.3390/electronics12040837 - 07 Feb 2023
Cited by 4 | Viewed by 1106
Abstract
The rise of social networks has greatly contributed to creating information cascades. Overtime, new nodes are added to the cascade network, which means the cascade network is dynamically variable. At the same time, there are often only a few nodes in the cascade [...] Read more.
The rise of social networks has greatly contributed to creating information cascades. Overtime, new nodes are added to the cascade network, which means the cascade network is dynamically variable. At the same time, there are often only a few nodes in the cascade network before new nodes join. Therefore, it becomes a key task to predict the diffusion after the dynamic cascade based on the small number of nodes observed in the previous period. However, existing methods are limited for dynamic short cascades and cannot combine temporal information with structural information well, so a new model, MetaCaFormer, based on meta-learning and the Transformer structure, is proposed in this paper for dynamic short cascade prediction. Considering the limited processing capability of traditional graph neural networks for temporal information, we propose a CaFormer model based on the Transformer structure, which inherits the powerful processing capability of Transformer for temporal information, while considering the neighboring nodes, edges and spatial importance of nodes, effectively combining temporal and structural information. At the same time, to improve the prediction ability for short cascades, we also fuse meta-learning so that it can be quickly adapted to short cascade data. In this paper, MetaCaFormer is applied to two publicly available datasets in different scenarios for experiments to demonstrate its effectiveness and generalization ability. The experimental results show that MetaCaFormer outperforms the currently available baseline methods. Full article
(This article belongs to the Special Issue Intelligent Data Analysis in Cyberspace)
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16 pages, 1754 KiB  
Article
Jointly Modeling Aspect Information and Ratings for Review Rating Prediction
Electronics 2022, 11(21), 3532; https://doi.org/10.3390/electronics11213532 - 29 Oct 2022
Viewed by 1081
Abstract
Although matrix model-based approaches to collaborative filtering (CF), such as latent factor models, achieve good accuracy in review rating prediction, they still face data sparsity problems. Many recent studies have exploited review text information to improve the performance of predictions. The review content [...] Read more.
Although matrix model-based approaches to collaborative filtering (CF), such as latent factor models, achieve good accuracy in review rating prediction, they still face data sparsity problems. Many recent studies have exploited review text information to improve the performance of predictions. The review content that they use, however, is usually on the coarse-grained text level or sentence level. In this paper, we propose a joint model that incorporates review text information with matrix factorization for review rating prediction. First, we adopt an aspect extraction method and propose a simple and practical algorithm to represent the review by aspects and sentiments. Then, we propose two similarity measures: aspect-based user similarity and aspect-based product similarity. Finally, aspect-based user and product similarity measures are incorporated into a matrix factorization to build a joint model for rating prediction. To this end, our model can alleviate the data sparsity problem and obtain interpretability for the recommendation. We conducted experiments on two datasets. The experimental results demonstrate the effectiveness of the proposed model. Full article
(This article belongs to the Special Issue Intelligent Data Analysis in Cyberspace)
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17 pages, 1662 KiB  
Article
IoT Anomaly Detection Based on Autoencoder and Bayesian Gaussian Mixture Model
Electronics 2022, 11(20), 3287; https://doi.org/10.3390/electronics11203287 - 12 Oct 2022
Cited by 4 | Viewed by 1693
Abstract
The Internet of Things (IoT) is increasingly providing industrial production objects to connect with the physical world and has been widely used in various fields. Although it has brought great industrial convenience, there are also potential security threats due to the vulnerabilities and [...] Read more.
The Internet of Things (IoT) is increasingly providing industrial production objects to connect with the physical world and has been widely used in various fields. Although it has brought great industrial convenience, there are also potential security threats due to the vulnerabilities and malicious nodes in IoT. To correctly identify the traffic of malicious nodes in IoT and reduce the damage caused by malicious attacks on IoT devices, this paper proposes an autoencoder-based IoT malicious node detection method. The contributions of this paper are as follows: firstly, the high complexity multi-featured traffic data are processed and dimensionally reduced through the autoencoder to obtain the low-dimensional feature data. Then, the Bayesian Gaussian mixture model is adopted to cluster the data in a low-dimensional space to detect anomalies. Furthermore, the method of variational inference is used to estimate the parameters in the Bayesian Gaussian mixture model. To evaluate our model’s effectiveness, we used a public dataset for our experiments. As a result, in the experiment, the proposed method achieves a high accuracy rate of 99% distinguishing normal and abnormal traffic with three-dimension data reduced by the autoencoder, and it establishes our model’s better detection performance compared with previous K-means and Gaussian Mixture Model (GMM) solutions. Full article
(This article belongs to the Special Issue Intelligent Data Analysis in Cyberspace)
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