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Special Issue "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: 30 April 2023 | Viewed by 995

Special Issue Editors

Dr. Yangyang Li
E-Mail Website
Guest Editor
National Engineering Research Center for Risk Perception and Prevention, China Academy of Electronics and Information Technology, Beijing 100041, China
Interests: big data; data science; cyber security; social networks
Dr. Pengyuan Zhou
E-Mail Website
Guest Editor
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

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. 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 2000 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 (2 papers)

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Research

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 378
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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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
Viewed by 382
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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