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Intelligent Analysis and Security Calculation of Multisource Data

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 17591

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Special Issue Editors

Prof. Dr. Chunying Zhang

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College of Science, North China University of Science and Technology, Tangshan 063000, China
Interests: complex network; machine learning; artificial intelligence; fundamentals of cyberspace security
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Jingfeng Guo

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College of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
Interests: complex network modeling theory and application; data mining theory and application; industrial internet and big data analysis

Dr. Xiao Pan

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Guest Editor

School of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
Interests: complex network modeling; spatio-temporal big data mining and computing; mobile computing; privacy protection

Prof. Fengchun Liu

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Guest Editor

College of Science, North China University of Science and Technology, Tangshan 063000, China
Interests: data mining; machine learning; social networking; cyberspace security

Prof. Dr. Aimin Yang

E-Mail Website
Guest Editor

College of Science, North China University of Science and Technology, Tangshan 063000, China
Interests: artificial intelligence; network security; big data modeling; numerical calculation; green metallurgy; precision medicine
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the continuous development of information technology, emerging technologies and services such as the Internet of Things, social networks, and the social Internet of Things are emerging. Data in various fields of scientific research and social life are being generated at an unprecedented rate and widely collected and stored. How to realize the intelligent processing of data to make full use of the knowledge and value contained in the data has become the consensus of the current academic and industrial circles. In particular, the existing multi-source heterogeneous, semantic complex, large-scale, dynamic, and changeable spatio-temporal big data, complex network data etc. bring new challenges to data intelligent processing methods. At the same time, as more and more data are collected, analyzed, and intelligentized, the risk of information leakage increases. Information security issues are unconsciously ignored in many areas of AI. Ensuring data security and information security while conducting intelligent data analysis has also attracted extensive attention from the government, industry, and academia.

This Special Issue is primarily interested in the complex heterogeneous and multisource dynamics of spatio-temporal big data, point cloud data, complex network data, video data, etc. It mainly discusses the fundamental theories and key technologies involved in data mining and prediction, knowledge extraction and reasoning, network modeling and visualization. It also pays attention to data security and information security in the process of data intelligence, including uncertainty theory methods, adversarial machine learning and privacy protection technologies.

Prof. Dr. Chunying Zhang
Prof. Dr. Jingfeng Guo
Dr. Xiao Pan
Prof. Fengchun Liu
Prof. Dr. Aimin Yang
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 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

spatio-temporal big data mining and prediction
large scale graph data analysis and knowledge extraction
point cloud data analysis and visualization
complex network modeling and application
granular computing and knowledge representation learning
computer vision and intelligent recognition
machine learning and adversarial machine learning
uncertainty theory and information security
image steganography and steganography analysis
big data security and privacy protection

Published Papers (12 papers)

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Research

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14 pages, 2099 KiB

Open AccessArticle

Research on a Decision Tree Classification Algorithm Based on Granular Matrices

by Lijuan Meng, Bin Bai, Wenda Zhang, Lu Liu and Chunying Zhang

Electronics 2023, 12(21), 4470; https://doi.org/10.3390/electronics12214470 - 30 Oct 2023

Viewed by 850

Abstract

The decision tree is one of the most important and representative classification algorithms in the field of machine learning, and it is an important technique for solving data mining classification tasks. In this paper, a decision tree classification algorithm based on granular matrices is proposed on the basis of granular computing theory. Firstly, the bit-multiplication and bit-sum operations of granular matrices are defined. The logical operations between granules are replaced by simple multiplication and addition operations, which reduces the operation time. Secondly, the similarity between granules is defined, the similarity metric matrix of the granular space is constructed, the classification actions are extracted from the similarity metric matrix, and the classification accuracy is defined by weighting the classification actions with the probability distribution of the granular space. Finally, the classification accuracy of the conditional attribute is used to select the splitting attributes of the decision tree as the nodes to form forks in the tree, and the similarity between granules is used to judge whether the data types in the sub-datasets are consistent to form the leaf nodes. The feasibility of the algorithm is demonstrated by means of case studies. The results of tests conducted on six UCI public datasets show that the algorithm has higher classification accuracy and better classification performance than the ID3 and C4.5. Full article

(This article belongs to the Special Issue Intelligent Analysis and Security Calculation of Multisource Data)

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16 pages, 5457 KiB

Open AccessArticle

Research on the Method of Hypergraph Construction of Information Systems Based on Set Pair Distance Measurement

by Jing Wang, Siwu Lan, Xiangyu Li, Meng Lu, Jingfeng Guo, Chunying Zhang and Bin Liu

Electronics 2023, 12(20), 4375; https://doi.org/10.3390/electronics12204375 - 23 Oct 2023

Cited by 1 | Viewed by 872

Abstract

As a kind of special graph of structured data, a hypergraph can intuitively describe not only the higher-order relation and complex connection mode between nodes but also the implicit relation between nodes. Aiming at the limitation of traditional distance measurement in high-dimensional data, a new method of hypergraph construction based on set pair theory is proposed in this paper. By means of dividing the relationship between data attributes, the set pair connection degree between samples is calculated, and the set pair distance between samples is obtained. Then, on the basis of set pair distance, the combination technique of k-nearest neighbor and ε radius is used to construct a hypergraph, and high-dimensional expression and hypergraph clustering are demonstrated experimentally. By performing experiments on different datasets on the Kaggle open-source dataset platform, the comparison of cluster purity, the Rand coefficient, and normalized mutual information are shown to demonstrate that this distance measurement method is more effective in high-dimensional expression and exhibits a more significant performance improvement in spectral clustering. Full article

(This article belongs to the Special Issue Intelligent Analysis and Security Calculation of Multisource Data)

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18 pages, 5379 KiB

Open AccessArticle

Similarity Distribution Density: An Optimized Approach to Outlier Detection

by Li Quan, Tao Gong and Kaida Jiang

Electronics 2023, 12(20), 4227; https://doi.org/10.3390/electronics12204227 - 12 Oct 2023

Viewed by 892

Abstract

When dealing with uncertain data, traditional model construction methods often ignore or filter out noise data to improve model performance. However, this simple approach can lead to insufficient data utilization, model bias, reduced detection ability, and decreased robustness of detection models. Outliers can be considered as data that are inconsistent with other patterns at certain specific moments and are not always negative data, so their emergence is not always bad. In the process of data analysis, outliers play a crucial role in sample vector recognition, missing value processing, and model stability verification. In addition, unsupervised models have very high computation costs when recognizing outliers, especially non-parameterized unsupervised models. To solve the above problems, we used semi-supervised learning processes and used similarity as a negative selection criterion to propose a local density verification detection model (Vd-LOD). This model establishes similarity pseudo-labels for multi-label and multi-type samples, verifies the accuracy of outlier values based on local outlier factors, and increases the detector’s sensitivity to outliers. The experimental results show that under different parameter settings with varying outlier quantities, Vd-LOD outperforms other detection models in terms of the significant increase in average time consumption caused by verifying the presence of relationships, while also achieving an approximate 6% improvement in average detection accuracy. Full article

(This article belongs to the Special Issue Intelligent Analysis and Security Calculation of Multisource Data)

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13 pages, 4750 KiB

Open AccessArticle

Diagnosis and Treatment Knowledge Graph Modeling Application Based on Chinese Medical Records

by Jianghan Wang, Zhu Qu, Yihan Hu, Qiyun Ling, Jingyi Yu and Yushan Jiang

Electronics 2023, 12(16), 3412; https://doi.org/10.3390/electronics12163412 - 11 Aug 2023

Cited by 1 | Viewed by 1150

Abstract

In this study, a knowledge graph of Chinese medical record data was constructed based on graph database technology. An entity extraction method based on natural language processing, disambiguation, and reorganization for Chinese medical records is proposed, and dictionaries of drugs and treatment plans are constructed. Examples of applications of the knowledge graph in diagnosis and treatment prediction are given. Experimentally, it is found that the knowledge graph based on the graph database is 116.7% faster than the traditional database in complex relational queries. Full article

(This article belongs to the Special Issue Intelligent Analysis and Security Calculation of Multisource Data)

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19 pages, 16835 KiB

Open AccessArticle

Improved BIGRU Model and Its Application in Stock Price Forecasting

by Yuanshuai Duan, Yuanxin Liu, Yi Wang, Shangsheng Ren and Yibo Wang

Electronics 2023, 12(12), 2718; https://doi.org/10.3390/electronics12122718 - 17 Jun 2023

Cited by 3 | Viewed by 1749

Abstract

In order to obtain better prediction results, this paper combines improved complete ensemble EMD (ICEEMDAN) and the whale algorithm of multi-objective optimization (MOWOA) to improve the bidirectional gated recurrent unit (BIGRU), which makes full use of original complex stock price time series data and improves the hyperparameters of the BIGRU network. To address the problem that BIGRU cannot make full use of the stationary data, the original sequence data are processed using the ICEEMDAN decomposition algorithm to derive the non-stationary and stationary parts of the data and modeled with the BIGRU and the autoregressive integrated moving average model (ARIMA), respectively. The modeling process introduces a whale algorithm for multi-objective optimization, which improves the probability of finding the best combination of parameter vectors. The

R^{2}

, MAPE, MSE, MAE, and RMSE values of the BIGRU algorithm, ICEEMDAN-BIGRU algorithm, MOWOA-BIGRU algorithm, and the improved algorithm were compared. An average improvement of 14.4% over the original algorithm’s goodness-of-fit value will greatly improve the accuracy of stock price predictions. Full article

(This article belongs to the Special Issue Intelligent Analysis and Security Calculation of Multisource Data)

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22 pages, 6002 KiB

Open AccessArticle

Study on Downhole Geomagnetic Suitability Problems Based on Improved Back Propagation Neural Network

by Xu Zhou, Jing Liu, Huiwen Men, Shangsheng Ren and Liwen Guo

Electronics 2023, 12(11), 2520; https://doi.org/10.3390/electronics12112520 - 02 Jun 2023

Viewed by 1025

Abstract

The analysis of geomagnetic suitability is the basis and premise of geomagnetic matching navigation and positioning. A geomagnetic suitability evaluation model using mixed sampling and an improved back propagation neural network (BPNN) based on the gray wolf optimization (GWO) algorithm by incorporating the dimension learning-based hunting (DLH) search strategy algorithm was proposed in this paper to accurately assess the geomagnetic suitability. Compared with the traditional geomagnetic suitability evaluation model, its generalization ability and accuracy were better improved. Firstly, the key indicators and matching labels used for geomagnetic suitability evaluation were analyzed, and an evaluation system was established. Then, a mixed sampling method based on the synthetic minority over-sampling technique (SMOTE) and Tomek Links was employed to extend the original dataset and construct a new dataset. Next, the dataset was divided into a training set and a test set, according to 7:3. The geomagnetic standard deviation, kurtosis coefficient, skewness coefficient, geomagnetic information entropy, geomagnetic roughness, variance of geomagnetic roughness, and correlation coefficient were used as input indicators and put into the DLH-GWO-BPNN model for model training with matching labels as output. Accuracy, recall, the ROC curve, and the AUC value were taken as evaluation indexes. Finally, PSO (Particle Swarm Optimization)-BPNN, WOA (Whale Optimization Algorithm)-BPNN, GA (Genetic Algorithm)-BPNN, and GWO-BPNN algorithms were selected as compared methods to verify the predictable ability of the DLH-GWO-BPNN. The accuracy ranking of the five models on the test set was as follows: PSO-BPNN (80.95 %) = WOA-BPNN (80.95%) < GA-BPNN (85.71%) = GWO-BPNN (85.71%) < DLH-GWO-BPNN (95.24%). The results indicate that the DLH-GWO-BPNN model can be used as a reliable method for underground geomagnetic suitability research, which can be applied to the research of geomagnetic matching navigation. Full article

(This article belongs to the Special Issue Intelligent Analysis and Security Calculation of Multisource Data)

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18 pages, 3999 KiB

Open AccessArticle

Improved Design and Application of Security Federation Algorithm

by Xiaolei Yang, Yongshan Liu, Jiabin Xie and Tianbao Hao

Electronics 2023, 12(6), 1375; https://doi.org/10.3390/electronics12061375 - 13 Mar 2023

Viewed by 974

Abstract

(1) Background: To avoid affecting the accuracy and practicability of the security federation model due to the geographical and environmental factors involved in each local model and set the corresponding weights for each local model, the local model parameters and weights participated in the calculation at the same time. (2) Methods: Apply the improved model to the income evaluation of taxi drivers. Multiple linear regression was used to fit the local model parameters, and the loss function value was calculated. Then, according to the improved security federation algorithm, the model parameters and local model weights were encrypted by using the Paillier homomorphic encryption algorithm, and the encrypted model parameter information was uploaded to the aggregation server for aggregation average. (3) Results: The experimental results show that after 1000 iterations, the accuracy curve converges in the interval [0.93, 0.97]; the mean accuracy value was 94.27%, and the mean loss function value was 1.0886. It was the same understanding that the mean value of the loss function calculated by the traditional model was 1.9910. (4) From the model and data, the accuracy of the improved model has been improved. It can better reflect the income of taxi drivers. Full article

(This article belongs to the Special Issue Intelligent Analysis and Security Calculation of Multisource Data)

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12 pages, 1547 KiB

Open AccessArticle

Spatio-Temporal Heterogeneous Graph Neural Networks for Estimating Time of Travel

by Lei Wu, Yong Tang, Pei Zhang and Ying Zhou

Electronics 2023, 12(6), 1293; https://doi.org/10.3390/electronics12061293 - 08 Mar 2023

Cited by 1 | Viewed by 1242

Abstract

Estimating Time of Travel (ETT) is a crucial element of intelligent transportation systems. In most previous studies, time of travel is estimated by identifying the spatio-temporal features of road segments or intersections independently. However, due to continuous changes in road segments and intersections in a path, dynamic features should be coupled and interactive. Therefore, employing only road segment or intersection features is inadequate for improving the accuracy of ETT. To address this issue, we proposed a novel deep learning framework for ETT based on a spatio-temporal heterogeneous graph neural network (STHGNN). Specifically, a heterogeneous traffic graph was first created based on intersections and road segments, which implies an adjacency correlation. Next, a learning approach for spatio-temporal heterogeneous convolutional attention networks was proposed to obtain the spatio-temporal correlations of joint intersections and road segments. This approach integrates temporal and spatial features. Finally, a fusion prediction approach was employed to estimate the travel time of a given path. Experiments were conducted on real-world path datasets to evaluate our proposed model. The results showed that STHGNN significantly outperformed the baselines. Full article

(This article belongs to the Special Issue Intelligent Analysis and Security Calculation of Multisource Data)

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22 pages, 601 KiB

Open AccessArticle

Efficient Reachability Ratio Computation for 2-Hop Labeling Scheme

by Xian Tang, Junfeng Zhou, Yunyu Shi, Xiang Liu and Lihong Kong

Electronics 2023, 12(5), 1178; https://doi.org/10.3390/electronics12051178 - 28 Feb 2023

Viewed by 854

Abstract

Reachability queries processing has been extensively studied during the past decades. Many approaches have followed the line of designing 2-hop labels to ensure acceleration. Considering its index size cannot be bounded, researchers have proposed to use a part of nodes to construct partial 2-hop labels (p2HLs) to cover as much reachability information as possible. We achieved better query performance using p2HLs with a limited index size and index construction time. However, the adoption of p2HLs was based on intuition, and the number of nodes used to generate p2HLs was fixed in advance blindly, without knowing its applicability. In this paper, we focused on the problem of efficiently computing a reachability ratio (RR) in order to obtain RR-aware p2HLs. Here, RR denoted the ratio of the number of reachable queries that could be answered by p2HLs over the total number of reachable queries involved in a given graph. Based on the RR, users could determine whether p2HLs should be used to answer the reachability queries for a given graph and how many nodes should be chosen to generate p2HLs. We discussed the difficulties of RR computation and propose an incremental-partition algorithm for RR computation. Our rich experimental results showed that our algorithm could efficiently obtain the RR and the overall effects on query performance by different p2HLs. Based on the experimental results, we provide our findings on the use p2HLs for a given graph for processing reachability queries. Full article

(This article belongs to the Special Issue Intelligent Analysis and Security Calculation of Multisource Data)

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12 pages, 1926 KiB

Open AccessArticle

Image Steganalysis of Low Embedding Rate Based on the Attention Mechanism and Transfer Learning

by Shouyue Liu, Chunying Zhang, Liya Wang, Pengchao Yang, Shaona Hua and Tong Zhang

Electronics 2023, 12(4), 969; https://doi.org/10.3390/electronics12040969 - 15 Feb 2023

Cited by 3 | Viewed by 1327

Abstract

In recent years, some research results have been achieved in the field of image steganalysis. However, there are still problems of difficulty in extracting steganographic features from images with low embedding rates and unsatisfactory detection performance of steganalysis. In this paper, we propose an image steganalysis method based on the attention mechanism and transfer learning. The method constructs a network model based on a convolutional neural network, including a preprocessing layer, a transposed convolutional layer, an ordinary convolutional layer, and a fully connected layer. We introduce the efficient channel attention module after the ordinary convolutional layer to focus on the steganographic region of the image, capture the local cross-channel interaction information, realize the adaptive adjustment of feature weights, and enhance the ability to extract steganographic features. Meanwhile, we apply the transfer learning method to use the training model parameters of high embedding rate images as the initialization parameters of the training model of the low embedding rate to achieve feature migration and further improve the steganalysis performance of the low embedding rate. The experimental results show that compared to the typical Xu-Net and Yedroudj-Net models, the detection accuracy of the proposed method is improved by 16.36% to 30.66% and by 35.59 to 37.83% for the embedding rates of 0.05 bpp, 0.1 bpp, and 0.2 bpp, respectively. Compared to the state-of-the-art Shen-Net model with low embedding rates, the detection accuracy is improved by 3.43% to 6.41%. This demonstrates the higher detection performance of the proposed method for steganalysis of low embedding rate images. Full article

(This article belongs to the Special Issue Intelligent Analysis and Security Calculation of Multisource Data)

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Review

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35 pages, 1939 KiB

Open AccessReview

A Review of Federated Meta-Learning and Its Application in Cyberspace Security

by Fengchun Liu, Meng Li, Xiaoxiao Liu, Tao Xue, Jing Ren and Chunying Zhang

Electronics 2023, 12(15), 3295; https://doi.org/10.3390/electronics12153295 - 31 Jul 2023

Cited by 2 | Viewed by 1862

Abstract

In recent years, significant progress has been made in the application of federated learning (FL) in various aspects of cyberspace security, such as intrusion detection, privacy protection, and anomaly detection. However, the robustness of federated learning in the face of malicious attacks (such us adversarial attacks, backdoor attacks, and poisoning attacks) is weak, and the unfair allocation of resources leads to slow convergence and inefficient communication efficiency regarding FL models. Additionally, the scarcity of malicious samples during FL model training and the heterogeneity of data result in a lack of personalization in FL models. These challenges pose significant obstacles to the application of federated learning in the field of cyberspace security. To address these issues, the introduction of meta-learning into federated learning has been proposed, resulting in the development of federated meta-learning models. These models aim to train personalized models for each client, reducing performance discrepancies across different clients and enhancing model fairness. In order to advance research on federated meta-learning and its applications in the field of cyberspace security, this paper first introduces the algorithms of federated meta-learning. Based on different usage principles, these algorithms are categorized into client-level personalization algorithms, network algorithms, prediction algorithms, and recommendation algorithms, and are thoroughly presented and analyzed. Subsequently, the paper divides current cyberspace security issues in the network domain into three branches: information content security, network security, and information system security. For each branch, the application research methods and achievements of federated meta-learning are elucidated and compared, highlighting the advantages and disadvantages of federated meta-learning in addressing different cyberspace security issues. Finally, the paper concludes with an outlook on the deep application of federated meta-learning in the field of cyberspace security. Full article

(This article belongs to the Special Issue Intelligent Analysis and Security Calculation of Multisource Data)

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Other

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39 pages, 1114 KiB

Open AccessSystematic Review

Crypto-Ransomware: A Revision of the State of the Art, Advances and Challenges

by José Antonio Gómez Hernández, Pedro García Teodoro, Roberto Magán Carrión and Rafael Rodríguez Gómez

Electronics 2023, 12(21), 4494; https://doi.org/10.3390/electronics12214494 - 01 Nov 2023

Cited by 1 | Viewed by 2535

Abstract

According to the premise that the first step to try to solve a problem is to deepen our knowledge of it as much as possible, this work is mainly aimed at diving into and understanding crypto-ransomware, a very present and true-world digital pandemic, from several perspectives. With this aim, this work contributes the following: (a) a review of the fundamentals of this security threat, typologies and families, attack model and involved actors, as well as lifecycle stages; (b) an analysis of the evolution of ransomware in the past years, and the main milestones regarding the development of new variants and real cases that have occurred; (c) a study of the most relevant and current proposals that have appeared to fight against this scourge, as organized in the usual defence lines (prevention, detection, response and recovery); and (d) a discussion of the current trends in ransomware infection and development as well as the main challenges that necessarily need to be dealt with to reduce the impact of crypto-ransomware. All of this will help to better understand the situation and, based on this, will help to develop more adequate defence procedures and effective solutions and tools to defeat attacks. Full article

(This article belongs to the Special Issue Intelligent Analysis and Security Calculation of Multisource Data)

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