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Applied Sciences
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New Insights and Perspectives in Cyber and Information Security
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New Insights and Perspectives in Cyber and Information Security

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 11587

Special Issue Editors

Dr. Bo Jiang

E-Mail Website
Guest Editor
Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100085, China
Interests: cybersecurity; data mining; machine learning
Prof. Dr. Wenjia Niu

E-Mail Website
Guest Editor
School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
Interests: cybersecurity; AI security
Prof. Dr. Wei Wang

E-Mail Website
Guest Editor
Data Science and Analytics Thrust, Information Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511458, China
Interests: big data; databases; machine learning; deep learning
Prof. Dr. Xiaobo Ma

E-Mail Website
Guest Editor
School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China
Interests: cybersecurity; internet measurement; traffic analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Yufei Han

E-Mail Website
Guest Editor
INRIA, Campus de Beaulieu, 263 Av. Général Leclerc, 35042 Rennes, France
Interests: security and privacy of AI systems; AI-based IDS and malware analysis

Special Issue Information

Dear Colleagues,

With network and information technology development, cyberspace has become the "fifth space" after land, sea, sky and outer spaces. The Internet, as an important infrastructure, brings great convenience to people's lives; however, it is also abused by various cybercriminal activities. For example, malicious behaviors such as botnet command and control, spam distribution, phishing, data theft, false information dissemination, etc. require network and information technology to initiate or hide malicious activities. Therefore, close cooperation among various stakeholders is needed to keep up with the changing threat situation and develop effective solutions for prevention detection and response.

This Special Issue will focus on cutting-edge original research and review articles from academia and opinions from industry to provide new insights and perspectives in network and information security research and practice. It will cover topics such as cybercrime, cyberwarfare, security modeling, privacy protection, artificial intelligence and data security.

Topics of interest include but are not limited to the following:

  • Computer, network, and cloud computing security
  • Cyber attack prevention, detection, investigation and response
  • Measurements for network anomalies detection
  • Security frameworks for various applications and scenarios
  • Security and information hiding in data mining
  • Big data security, privacy and trust
  • Online social media security, privacy and trust
  • Blockchain security, privacy and trust
  • Security and privacy for Internet of Things
  • Security and privacy for Artificial Intelligence

Dr. Bo Jiang
Prof. Dr. Wenjia Niu
Prof. Dr. Wei Wang
Prof. Dr. Xiaobo Ma
Dr. Yufei Han
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. Applied Sciences 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

  • computer security
  • network security
  • information security
  • cloud computing security
  • anomaly detection
  • risk evaluation
  • privacy preservation
  • security management
  • protocol security

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (6 papers)

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Research

11 pages, 1358 KiB  
Article
Camouflage Backdoor Attack against Pedestrian Detection
by Yalun Wu, Yanfeng Gu, Yuanwan Chen, Xiaoshu Cui, Qiong Li, Yingxiao Xiang, Endong Tong, Jianhua Li, Zhen Han and Jiqiang Liu
Appl. Sci. 2023, 13(23), 12752; https://doi.org/10.3390/app132312752 - 28 Nov 2023
Cited by 3 | Viewed by 1547
Abstract
Pedestrian detection models in autonomous driving systems heavily rely on deep neural networks (DNNs) to perceive their surroundings. Recent research has unveiled the vulnerability of DNNs to backdoor attacks, in which malicious actors manipulate the system by embedding specific triggers within the training [...] Read more.
Pedestrian detection models in autonomous driving systems heavily rely on deep neural networks (DNNs) to perceive their surroundings. Recent research has unveiled the vulnerability of DNNs to backdoor attacks, in which malicious actors manipulate the system by embedding specific triggers within the training data. In this paper, we propose a tailored camouflaged backdoor attack method designed for pedestrian detection in autonomous driving systems. Our approach begins with the construction of a set of trigger-embedded images. Subsequently, we employ an image scaling function to seamlessly integrate these trigger-embedded images into the original benign images, thereby creating potentially poisoned training images. Importantly, these potentially poisoned images exhibit minimal discernible differences from the original benign images and are virtually imperceptible to the human eye. We then strategically activate these concealed backdoors in specific scenarios, causing the pedestrian detection models to make incorrect judgments. Our study demonstrates that once our attack successfully embeds the backdoor into the target model, it can deceive the model into failing to detect any pedestrians marked with our trigger patterns. Extensive evaluations conducted on a publicly available pedestrian detection dataset confirm the effectiveness and stealthiness of our camouflaged backdoor attacks. Full article
(This article belongs to the Special Issue New Insights and Perspectives in Cyber and Information Security)
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14 pages, 866 KiB  
Article
Enhancing Predictive Expert Method for Link Prediction in Heterogeneous Information Social Networks
by Jianjun Wu, Yuxue Hu, Zhongqiang Huang, Junsong Li, Xiang Li and Ying Sha
Appl. Sci. 2023, 13(22), 12437; https://doi.org/10.3390/app132212437 - 17 Nov 2023
Viewed by 1602
Abstract
Link prediction is a critical prerequisite and foundation task for social network security that involves predicting the potential relationship between nodes within a network or graph. Although the existing methods show promising performance, they often ignore the unique attributes of each link type [...] Read more.
Link prediction is a critical prerequisite and foundation task for social network security that involves predicting the potential relationship between nodes within a network or graph. Although the existing methods show promising performance, they often ignore the unique attributes of each link type and the impact of diverse node differences on network topology when dealing with heterogeneous information networks (HINs), resulting in inaccurate predictions of unobserved links. To overcome this hurdle, we propose the Enhancing Predictive Expert Method (EPEM), a comprehensive framework that includes an individual feature projector, a predictive expert constructor, and a trustworthiness investor. The individual feature projector extracts the distinct characteristics associated with each link type, eliminating shared attributes that are common across all links. The predictive expert constructor then creates enhancing predictive experts, which improve predictive precision by incorporating the individual feature representations unique to each node category. Finally, the trustworthiness investor evaluates the reliability of each enhancing predictive expert and adjusts their contributions to the prediction outcomes accordingly. Our empirical evaluations on three diverse heterogeneous social network datasets demonstrate the effectiveness of EPEM in forecasting unobserved links, outperforming the state-of-the-art methods. Full article
(This article belongs to the Special Issue New Insights and Perspectives in Cyber and Information Security)
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20 pages, 2594 KiB  
Article
Fine-Auth: A Fine-Grained User Authentication and Key Agreement Protocol Based on Physical Unclonable Functions for Wireless Body Area Networks
by Kaijun Liu, Qiang Cao, Guosheng Xu and Guoai Xu
Appl. Sci. 2023, 13(22), 12376; https://doi.org/10.3390/app132212376 - 15 Nov 2023
Viewed by 1254
Abstract
Wireless body area networks (WBANs) can be used to realize the real-time monitoring and transmission of health data concerning the human body based on wireless communication technology. With the transmission of these sensitive health data, security and privacy protection issues have become increasingly [...] Read more.
Wireless body area networks (WBANs) can be used to realize the real-time monitoring and transmission of health data concerning the human body based on wireless communication technology. With the transmission of these sensitive health data, security and privacy protection issues have become increasingly prominent. Fine-grained authentication allows physicians to run authentication checks of another specific entity according to their identifying attributes. Hence, it plays a key role in preserving the security and privacy of WBANs. In recent years, substantial research has been carried out on fine-grained authentication. However, these studies have put considerable effort into WBAN performances, resulting in weakened security. This paper proposes a fine-grained user authentication and key agreement protocol based on physical unclonable functions (PUFs) while maintaining robust security and performance. This will allow physicians to perform mutual authentication and obtain key agreements with authorized body area sensor nodes according to their identity parameters, such as occupation type and title. We then provide comprehensive security and heuristic analyses to demonstrate the security of the proposed protocol. Finally, the performance comparison shows that the proposed protocol is more robust in security, cost-effective communication, and computational overheads compared to three leading alternatives. Full article
(This article belongs to the Special Issue New Insights and Perspectives in Cyber and Information Security)
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22 pages, 3504 KiB  
Article
A Comparative Analysis of Active Learning for Rumor Detection on Social Media Platforms
by Feng Yi, Hongsheng Liu, Huaiwen He and Lei Su
Appl. Sci. 2023, 13(22), 12098; https://doi.org/10.3390/app132212098 - 7 Nov 2023
Cited by 1 | Viewed by 2177
Abstract
In recent years, the ubiquity of social networks has transformed them into essential platforms for information dissemination. However, the unmoderated nature of social networks and the advent of advanced machine learning techniques, including generative models such as GPT and diffusion models, have facilitated [...] Read more.
In recent years, the ubiquity of social networks has transformed them into essential platforms for information dissemination. However, the unmoderated nature of social networks and the advent of advanced machine learning techniques, including generative models such as GPT and diffusion models, have facilitated the propagation of rumors, posing challenges to society. Detecting and countering these rumors to mitigate their adverse effects on individuals and society is imperative. Automatic rumor detection, typically framed as a binary classification problem, predominantly relies on supervised machine learning models, necessitating substantial labeled data; yet, the scarcity of labeled datasets due to the high cost of fact-checking and annotation hinders the application of machine learning for rumor detection. In this study, we address this challenge through active learning. We assess various query strategies across different machine learning models and datasets in order to offer a comparative analysis. Our findings reveal that active learning reduces labeling time and costs while achieving comparable rumor detection performance. Furthermore, we advocate for the use of machine learning models with nonlinear classification boundaries on complex environmental datasets for more effective rumor detection. Full article
(This article belongs to the Special Issue New Insights and Perspectives in Cyber and Information Security)
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16 pages, 398 KiB  
Article
Deep Learning-Enabled Heterogeneous Transfer Learning for Improved Network Attack Detection in Internal Networks
by Gang Wang, Dong Liu, Chunrui Zhang and Teng Hu
Appl. Sci. 2023, 13(21), 12033; https://doi.org/10.3390/app132112033 - 4 Nov 2023
Viewed by 2033
Abstract
Cybersecurity faces constant challenges from increasingly sophisticated network attacks. Recent research shows machine learning can improve attack detection by training models on large labeled datasets. However, obtaining sufficient labeled data is difficult for internal networks. We propose a deep transfer learning model to [...] Read more.
Cybersecurity faces constant challenges from increasingly sophisticated network attacks. Recent research shows machine learning can improve attack detection by training models on large labeled datasets. However, obtaining sufficient labeled data is difficult for internal networks. We propose a deep transfer learning model to learn common knowledge from domains with different features and distributions. The model has two feature projection networks to transform heterogeneous features into a common space, and a classification network then predicts transformed features into labels. To align probability distributions for two domains, maximum mean discrepancy (MMD) is used to compute distribution distance alongside classification loss. Though the target domain only has a few labeled samples, unlabeled samples are adequate for computing MMD to align unconditional distributions. In addition, we apply a soft classification scheme on unlabeled data to compute MMD over classes to further align conditional distributions. Experiments between NSL-KDD, UNSW-NB15, and CICIDS2017 validate that the method substantially improves cross-domain network attack detection accuracy. Full article
(This article belongs to the Special Issue New Insights and Perspectives in Cyber and Information Security)
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19 pages, 1928 KiB  
Article
Knowledge-Graph- and GCN-Based Domain Chinese Long Text Classification Method
by Yifei Wang, Yongwei Wang, Hao Hu, Shengnan Zhou and Qinwu Wang
Appl. Sci. 2023, 13(13), 7915; https://doi.org/10.3390/app13137915 - 6 Jul 2023
Cited by 5 | Viewed by 2135
Abstract
In order to solve the current problems in domain long text classification tasks, namely, the long length of a document, which makes it difficult for the model to capture key information, and the lack of expert domain knowledge, which leads to insufficient classification [...] Read more.
In order to solve the current problems in domain long text classification tasks, namely, the long length of a document, which makes it difficult for the model to capture key information, and the lack of expert domain knowledge, which leads to insufficient classification accuracy, a domain long text classification model based on a knowledge graph and a graph convolutional neural network is proposed. BERT is used to encode the text, and each word’s corresponding vector is used as a node for the graph convolutional neural network so that the initialized vector contains rich semantic information. Using the trained entity–relationship extraction model, the entity-to-entity–relationships in the document are extracted and used as the edges of the graph convolutional neural network, together with syntactic dependency information. The graph structure mask is used to learn about edge relationships and edge types to further enhance the learning ability of the model for semantic dependencies between words. The method further improves the accuracy of domain long text classification by fusing knowledge features and data features. Experiments on three long text classification datasets—IFLYTEK, THUCNews, and the Chinese corpus of Fudan University—show accuracy improvements of 8.8%, 3.6%, and 2.6%, respectively, relative to the BERT model. Full article
(This article belongs to the Special Issue New Insights and Perspectives in Cyber and Information Security)
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