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Security and Privacy in Large-Scale Data Networks
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Security and Privacy in Large-Scale Data Networks

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (15 February 2023) | Viewed by 25909

Special Issue Editors

Prof. Dr. Hao Peng

E-Mail Website
Guest Editor
College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua 321004, China
Interests: IoT security; Artificial Intelligence Security; Social Networks Security
Dr. Zheli Liu

E-Mail Website
Guest Editor
College of Cyber Science, Nankai University, Tianjin 300350, China
Interests: data security; artificial intelligence security
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jin Li

E-Mail Website
Guest Editor
School of Computer Science and Network Engineering, Guangzhou University, Guangzhou 510275, China
Interests: Artificial Intelligence Security; Data security; Cyberspace Security
Prof. Dr. Yang Xiang

E-Mail Website
Guest Editor
School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne 3122, Australia
Interests: Cyberspace Security; Artificial Intelligence Security

Special Issue Information

Dear Colleagues,

With the advent of the Internet of Everything era, large-scale data networks represented by social networks and the Internet of Things have penetrated into all aspects of our daily lives, and it may completely change the interconnection and interdependence of the Internet. It is undeniable that the large-scale data networks represented by social networks greatly facilitate everyone's daily life and greatly expand their social networks. Large-scale systems such as social networks and the Internet of Things produce big data that commonly used computer software and hardware cannot capture, manage, and process within a tolerable amount of elapsed time.

However, there remain challenges that need to be addressed due to the complexity and heterogeneity of large-scale data networks such as social networks and the Internet of Things. These challenges include security, privacy, and communication security, a lack of which can lead to various attacks, including the spread of malicious code and the leakage of key personal information. In recent years, researchers have developed advanced technologies to analyze, detect, and prevent these potential threats, such as Sybil attacks and social spam. The purpose of this Special Issue is to promote the study of security issues in large-scale data networks. Ensuring that large-scale data networks such as social networks and the Internet of Things are secure has become a topic of increasing interest to both researchers and developers from academic fields and industries worldwide.

Prof. Dr. Hao Peng
Dr. Zheli Liu
Prof. Dr. Jin Li
Prof. Dr. Yang Xiang
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. Sensors 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 2600 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

  • data security for mobile internet and social networks
  • privacy protection in mobile internet and social networks
  • risk assessment in social networks and the Internet of Things (IoT)
  • reliability analysis for social networks and the IoT
  • data mining, knowledge discovery, and machine learning for social networks
  • security protocols and formal analysis for social networks and the IoT
  • anonymous communication in mobile social networks and the IoT
  • blockchain applications for social networks and the IoT
  • artificial-intelligence-based security for social networks and the IoT
  • safety management for social networks and the IoT

Published Papers (6 papers)

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Research

16 pages, 814 KiB  
Article
A Lightweight Authentication and Key Agreement Protocol for IoT-Enabled Smart Grid System
by Chen Chen, Hua Guo, Yapeng Wu, Bowen Shen, Mingyang Ding and Jianwei Liu
Sensors 2023, 23(8), 3991; https://0-doi-org.brum.beds.ac.uk/10.3390/s23083991 - 14 Apr 2023
Cited by 1 | Viewed by 1318
Abstract
The IoT-enabled Smart Grid uses IoT smart devices to collect the private electricity data of consumers and send it to service providers over the public network, which leads to some new security problems. To ensure the communication security in a smart grid, many [...] Read more.
The IoT-enabled Smart Grid uses IoT smart devices to collect the private electricity data of consumers and send it to service providers over the public network, which leads to some new security problems. To ensure the communication security in a smart grid, many researches are focusing on using authentication and key agreement protocols to protect against cyber attacks. Unfortunately, most of them are vulnerable to various attacks. In this paper, we analyze the security of an existent protocol by introducing an insider attacker, and show that their scheme cannot guarantee the claimed security requirements under their adversary model. Then, we present an improved lightweight authentication and key agreement protocol, which aims to enhance the security of IoT-enabled smart grid systems. Furthermore, we proved the security of the scheme under the real-or-random oracle model. The result shown that the improved scheme is secure in the presence of both internal attackers and external attackers. Compared with the original protocol, the new protocol is more secure, while keeping the same computation efficiency. Both of them are 0.0552 ms. The communication of the new protocol is 236 bytes, which is acceptable in smart grids. In other words, with similar communication and computation cost, we proposed a more secure protocol for smart grids. Full article
(This article belongs to the Special Issue Security and Privacy in Large-Scale Data Networks)
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24 pages, 3183 KiB  
Article
Dynamic Feature Dataset for Ransomware Detection Using Machine Learning Algorithms
by Juan A. Herrera-Silva and Myriam Hernández-Álvarez
Sensors 2023, 23(3), 1053; https://0-doi-org.brum.beds.ac.uk/10.3390/s23031053 - 17 Jan 2023
Cited by 16 | Viewed by 7052
Abstract
Ransomware-related cyber-attacks have been on the rise over the last decade, disturbing organizations considerably. Developing new and better ways to detect this type of malware is necessary. This research applies dynamic analysis and machine learning to identify the ever-evolving ransomware signatures using selected [...] Read more.
Ransomware-related cyber-attacks have been on the rise over the last decade, disturbing organizations considerably. Developing new and better ways to detect this type of malware is necessary. This research applies dynamic analysis and machine learning to identify the ever-evolving ransomware signatures using selected dynamic features. Since most of the attributes are shared by diverse ransomware-affected samples, our study can be used for detecting current and even new variants of the threat. This research has the following objectives: (1) Execute experiments with encryptor and locker ransomware combined with goodware to generate JSON files with dynamic parameters using a sandbox. (2) Analyze and select the most relevant and non-redundant dynamic features for identifying encryptor and locker ransomware from goodware. (3) Generate and make public a dynamic features dataset that includes these selected parameters for samples of different artifacts. (4) Apply the dynamic feature dataset to obtain models with machine learning algorithms. Five platforms, 20 ransomware, and 20 goodware artifacts were evaluated. The final feature dataset is composed of 2000 registers of 50 characteristics each. This dataset allows for a machine learning detection with a 10-fold cross-evaluation with an average accuracy superior to 0.99 for gradient boosted regression trees, random forest, and neural networks. Full article
(This article belongs to the Special Issue Security and Privacy in Large-Scale Data Networks)
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15 pages, 918 KiB  
Article
Multimodal Feature Fusion Method for Unbalanced Sample Data in Social Network Public Opinion
by Jian Zhao, Wenhua Dong, Lijuan Shi, Wenqian Qiang, Zhejun Kuang, Dawei Xu and Tianbo An
Sensors 2022, 22(15), 5528; https://0-doi-org.brum.beds.ac.uk/10.3390/s22155528 - 25 Jul 2022
Cited by 3 | Viewed by 2563
Abstract
With the wide application of social media, public opinion analysis in social networks has been unable to be met through text alone because the existing public opinion information includes data information of various modalities, such as voice, text, and facial expressions. Therefore multi-modal [...] Read more.
With the wide application of social media, public opinion analysis in social networks has been unable to be met through text alone because the existing public opinion information includes data information of various modalities, such as voice, text, and facial expressions. Therefore multi-modal emotion analysis is the current focus of public opinion analysis. In addition, multi-modal emotion recognition of speech is an important factor restricting the multi-modal emotion analysis. In this paper, the emotion feature retrieval method for speech is firstly explored and the processing method of sample disequilibrium data is then analyzed. By comparing and studying the different feature fusion methods of text and speech, respectively, the multi-modal feature fusion method for sample disequilibrium data is proposed to realize multi-modal emotion recognition. Experiments are performed using two publicly available datasets (IEMOCAP and MELD), which shows that processing multi-modality data through this method can obtain good fine-grained emotion recognition results, laying a foundation for subsequent social public opinion analysis. Full article
(This article belongs to the Special Issue Security and Privacy in Large-Scale Data Networks)
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25 pages, 3345 KiB  
Article
Topic-Emotion Propagation Mechanism of Public Emergencies in Social Networks
by Meng Cai, Han Luo, Xiao Meng and Ying Cui
Sensors 2021, 21(13), 4516; https://0-doi-org.brum.beds.ac.uk/10.3390/s21134516 - 01 Jul 2021
Cited by 9 | Viewed by 2967
Abstract
The information propagation of emergencies in social networks is often accompanied by the dissemination of the topic and emotion. As a virtual sensor of public emergencies, social networks have been widely used in data mining, knowledge discovery, and machine learning. From the perspective [...] Read more.
The information propagation of emergencies in social networks is often accompanied by the dissemination of the topic and emotion. As a virtual sensor of public emergencies, social networks have been widely used in data mining, knowledge discovery, and machine learning. From the perspective of network, this study aims to explore the topic and emotion propagation mechanism, as well as the interaction and communication relations of the public in social networks under four types of emergencies, including public health events, accidents and disasters, social security events, and natural disasters. Event topics were identified by Word2vec and K-means clustering. The biLSTM model was used to identify emotion in posts. The propagation maps of topic and emotion were presented visually on the network, and the synergistic relationship between topic and emotion propagation as well as the communication characteristics of multiple subjects were analyzed. The results show that there were similarities and differences in the propagation mechanism of topic and emotion in different types of emergencies. There was a positive correlation between topic and emotion of different types of users in social networks in emergencies. Users with a high level of topic influence were often accompanied by a high level of emotion appeal. Full article
(This article belongs to the Special Issue Security and Privacy in Large-Scale Data Networks)
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22 pages, 2594 KiB  
Article
A Framework of Vehicular Security and Demand Service Prediction Based on Data Analysis Integrated with Blockchain Approach
by Zeinab Shahbazi and Yung-Cheol Byun
Sensors 2021, 21(10), 3314; https://0-doi-org.brum.beds.ac.uk/10.3390/s21103314 - 11 May 2021
Cited by 13 | Viewed by 3299
Abstract
The prediction of taxi demand service has become a recently attractive area of research along with large-scale and potential applications in the intelligent transportation system. The demand process is divided into two main parts: Picking-up and dropping-off demand based on passenger habit. Taxi [...] Read more.
The prediction of taxi demand service has become a recently attractive area of research along with large-scale and potential applications in the intelligent transportation system. The demand process is divided into two main parts: Picking-up and dropping-off demand based on passenger habit. Taxi demand prediction is a great concept for drivers and passengers, and is designed platforms for ride-hailing and municipal managers. The majority of research has focused on forecasting the pick-up part of demand service and specifying the interconnection of spatial and temporal correlations. In this study, the main focus is to overcome the access point of non-registered users for having fake transactions using taxi services and predicting taxi demand pick-up and drop-off information. The integration of machine learning techniques and blockchain framework is considered a possible solution for this problem. The blockchain technique was selected as an effective technique for protecting and controlling the real-time system. Historical data analysis was processed by extracting the three higher related sections for the intervening time, namely closeness and trend. Next, the pick-up and drop-off taxi prediction task was processed based on constructing the components of multi-task learning and spatiotemporal feature extraction. The combination of feature embedding performance and Long Short-Term Memory (LSTM) obtain the pick-up and drop-off correlation by fusing the historical data spatiotemporal features. Finally, the taxi demand pick-up and drop-off prediction were processed based on the combination of the external factors. The experimental result is based on a real dataset in Jeju Island, South Korea, to show the proposed system’s efficacy and performance compared with other state-of-art models. Full article
(This article belongs to the Special Issue Security and Privacy in Large-Scale Data Networks)
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21 pages, 755 KiB  
Article
Sequential Model Based Intrusion Detection System for IoT Servers Using Deep Learning Methods
by Ming Zhong, Yajin Zhou and Gang Chen
Sensors 2021, 21(4), 1113; https://0-doi-org.brum.beds.ac.uk/10.3390/s21041113 - 05 Feb 2021
Cited by 63 | Viewed by 6883
Abstract
IoT plays an important role in daily life; commands and data transfer rapidly between the servers and objects to provide services. However, cyber threats have become a critical factor, especially for IoT servers. There should be a vigorous way to protect the network [...] Read more.
IoT plays an important role in daily life; commands and data transfer rapidly between the servers and objects to provide services. However, cyber threats have become a critical factor, especially for IoT servers. There should be a vigorous way to protect the network infrastructures from various attacks. IDS (Intrusion Detection System) is the invisible guardian for IoT servers. Many machine learning methods have been applied in IDS. However, there is a need to improve the IDS system for both accuracy and performance. Deep learning is a promising technique that has been used in many areas, including pattern recognition, natural language processing, etc. The deep learning reveals more potential than traditional machine learning methods. In this paper, sequential model is the key point, and new methods are proposed by the features of the model. The model can collect features from the network layer via tcpdump packets and application layer via system routines. Text-CNN and GRU methods are chosen because the can treat sequential data as a language model. The advantage compared with the traditional methods is that they can extract more features from the data and the experiments show that the deep learning methods have higher F1-score. We conclude that the sequential model-based intrusion detection system using deep learning method can contribute to the security of the IoT servers. Full article
(This article belongs to the Special Issue Security and Privacy in Large-Scale Data Networks)
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