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Future Internet
Special Issues
Security and Community Detection in Social Network
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Security and Community Detection in Social Network

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Cybersecurity".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 21008

Special Issue Editor

Prof. Dr. Tinghuai Ma

E-Mail Website
Guest Editor
Department of Computer Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: data mining; social network; privacy preserving; data sharing

Special Issue Information

Dear Colleagues,

With the rapid development of mobile devices and Internet of Things, social networks have become an important tool for people to communicate with each other, and has become a part of humans’ daily lives. The social network has a huge amount of information, including structured and unstructured information, which are valuable for network management, commercial interests, and politics. Recognizing the surname, keeping the personal information safe, and finding the connection between users are most important for social networks. The methods and algorithms include: personality profiling, community detection, information security, information publishing, privacy protected, graph mining, node classification, location based service, etc. The aim of this Special Issue is to report on the contributions of social network analysis to support social network development.

Prof. Dr. Tinghuai Ma
Guest Editor

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. Future Internet is an international peer-reviewed open access monthly 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 1600 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

  • information security
  • differential privacy
  • anonymization
  • personality profiling
  • graph classification
  • graph clustering
  • community detection
  • graph matching
  • graph algorithm parallelization

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Published Papers (3 papers)

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Research

15 pages, 3082 KiB  
Article
BERT- and BiLSTM-Based Sentiment Analysis of Online Chinese Buzzwords
by Xinlu Li, Yuanyuan Lei and Shengwei Ji
Future Internet 2022, 14(11), 332; https://doi.org/10.3390/fi14110332 - 14 Nov 2022
Cited by 29 | Viewed by 7006
Abstract
Sentiment analysis of online Chinese buzzwords (OCBs) is important for healthy development of platforms, such as games and social networking, which can avoid transmission of negative emotions through prediction of users’ sentiment tendencies. Buzzwords have the characteristics of varying text length, irregular wording, [...] Read more.
Sentiment analysis of online Chinese buzzwords (OCBs) is important for healthy development of platforms, such as games and social networking, which can avoid transmission of negative emotions through prediction of users’ sentiment tendencies. Buzzwords have the characteristics of varying text length, irregular wording, ignoring syntactic and grammatical requirements, no complete semantic structure, and no obvious sentiment features. This results in interference and challenges to the sentiment analysis of such texts. Sentiment analysis also requires capturing effective sentiment features from deeper contextual information. To solve the above problems, we propose a deep learning model combining BERT and BiLSTM. The goal is to generate dynamic representations of OCB vectors in downstream tasks by fine-tuning the BERT model and to capture the rich information of the text at the embedding layer to solve the problem of static representations of word vectors. The generated word vectors are then transferred to the BiLSTM model for feature extraction to obtain the local and global semantic features of the text while highlighting the text sentiment polarity for sentiment classification. The experimental results show that the model works well in terms of the comprehensive evaluation index F1. Our model also has important significance and research value for sentiment analysis of irregular texts, such as OCBs. Full article
(This article belongs to the Special Issue Security and Community Detection in Social Network)
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17 pages, 1472 KiB  
Article
A Novel Logo Identification Technique for Logo-Based Phishing Detection in Cyber-Physical Systems
by Padmalochan Panda, Alekha Kumar Mishra and Deepak Puthal
Future Internet 2022, 14(8), 241; https://doi.org/10.3390/fi14080241 - 15 Aug 2022
Cited by 7 | Viewed by 3523
Abstract
The first and foremost task of a phishing-detection mechanism is to confirm the appearance of a suspicious page that is similar to a genuine site. Once this is found, a suitable URL analysis mechanism may lead to conclusions about the genuineness of the [...] Read more.
The first and foremost task of a phishing-detection mechanism is to confirm the appearance of a suspicious page that is similar to a genuine site. Once this is found, a suitable URL analysis mechanism may lead to conclusions about the genuineness of the suspicious page. To confirm appearance similarity, most of the approaches inspect the image elements of the genuine site, such as the logo, theme, font color and style. In this paper, we propose a novel logo-based phishing-detection mechanism that characterizes the existence and unique distribution of hue values in a logo image as the foundation to unambiguously represent a brand logo. Using the proposed novel feature, the detection mechanism optimally classifies a suspicious logo to the best matching brand logo. The experiment is performed over our customized dataset based on the popular phishing brands in the South-Asia region. A set of five machine-learning algorithms is used to train and test the prepared dataset. We inferred from the experimental results that the ensemble random forest algorithm achieved the high accuracy of 87% with our prepared dataset. Full article
(This article belongs to the Special Issue Security and Community Detection in Social Network)
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14 pages, 1814 KiB  
Article
Creating Honeypots to Prevent Online Child Exploitation
by Joel Scanlan, Paul A. Watters, Jeremy Prichard, Charlotte Hunn, Caroline Spiranovic and Richard Wortley
Future Internet 2022, 14(4), 121; https://doi.org/10.3390/fi14040121 - 14 Apr 2022
Cited by 3 | Viewed by 9237
Abstract
Honeypots have been a key tool in controlling and understanding digital crime for several decades. The tool has traditionally been deployed against actors who are attempting to hack into systems or as a discovery mechanism for new forms of malware. This paper presents [...] Read more.
Honeypots have been a key tool in controlling and understanding digital crime for several decades. The tool has traditionally been deployed against actors who are attempting to hack into systems or as a discovery mechanism for new forms of malware. This paper presents a novel approach to using a honeypot architecture in conjunction with social networks to respond to non-technical digital crimes. The tool is presented within the context of Child Exploitation Material (CEM), and to support the goal of taking an educative approach to Internet users who are developing an interest in this material. The architecture that is presented in the paper includes multiple layers, including recruitment, obfuscation, and education. The approach does not aim to collect data to support punitive action, but to educate users, increasing their knowledge and awareness of the negative impacts of such material. Full article
(This article belongs to the Special Issue Security and Community Detection in Social Network)
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