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Emerging Research on Neural Networks and Anomaly Detection
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Emerging Research on Neural Networks and Anomaly Detection

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 14888

Special Issue Editors

Dr. Jiaping Gui

E-Mail Website
Guest Editor
School of Cyber Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: network and system security; artificial intelligence; software engineering
Dr. Futai Zou

E-Mail Website
Guest Editor
School of Cyber Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: network and system security

Special Issue Information

Dear Colleagues,

In this Special Issue, we aim to present the latest research trends in neural networks and anomaly detection. Particularly, we encourage the innovative application of neural networks to anomaly detection in the real world, which has been an active research area over the past few decades. Traditional machine learning-based or statistical solutions have been designed to achieve anomaly detection. However, it is challenging to apply traditional solutions to solve complex problems in various scenarios since these solutions require explicit feature extraction that typically fails to learn implicit relationships among features in the latent space. This issue has become a bottleneck to the improvement of the performance of traditional solutions when applied to anomaly detection.

Neural networks, also known as artificial neural networks or simulated neural networks, have been proposed as a promising solution for the detection of anomalies. In many cases, thanks to the capability of modeling and learning of the latent feature space, neural networks achieve a significantly better performance than that of the aforementioned traditional solutions. More specifically, solutions based on neural networks such as convolutional neural network, recurrent neural network, and autoencoder neural network have been leveraged to detect anomalies among various types of inputs, such as image, audio, and video. Large language model-based solutions are one of the emerging directions that combine advanced techniques (e.g., embedding representations, attention mechanism) to address practically challenging problems that remain in the real world.

Through this Special Issue, we hope to provide a collection of emerging research into neural networks that inspires researchers in both academia and industry to address challenges in anomaly detection. We welcome research studies on relevant topics including (but not limited to) network security, system security, mobile platforms, explainable AI, and privacy, among others.

Dr. Jiaping Gui
Dr. Futai Zou
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. Information 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

  • neural networks
  • supervised learning
  • unsupervised learning
  • anomaly detection
  • outlier detection
  • machine learning

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Published Papers (7 papers)

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Research

13 pages, 609 KiB  
Article
ADFilter—A Web Tool for New Physics Searches with Autoencoder-Based Anomaly Detection Using Deep Unsupervised Neural Networks
by Sergei V. Chekanov, Wasikul Islam, Rui Zhang and Nicholas Luongo
Information 2025, 16(4), 258; https://doi.org/10.3390/info16040258 - 22 Mar 2025
Viewed by 263
Abstract
A web-based tool called ADFilter (short for Anomaly Detection Filter) was developed to process collision events using autoencoders based on a deep unsupervised neural network. The autoencoders are trained on a small fraction of either collision data or Standard Model (SM) Monte Carlo [...] Read more.
A web-based tool called ADFilter (short for Anomaly Detection Filter) was developed to process collision events using autoencoders based on a deep unsupervised neural network. The autoencoders are trained on a small fraction of either collision data or Standard Model (SM) Monte Carlo simulations. The tool calculates loss distributions for input events, helping to determine the degree to which the events can be considered anomalous with respect to the SM events used for training. Therefore, it can be used for new physics searches in collider experiments. Real-life examples are provided to demonstrate how the tool can be used to reinterpret existing results from the Large Hadron Collider (LHC), with the goal of significantly improving exclusion limits. This tool is expected to mitigate the “reproducibility crisis” associated with various machine learning techniques, as it can incorporate machine learning approaches from third-party publications, making them accessible to the general public. Full article
(This article belongs to the Special Issue Emerging Research on Neural Networks and Anomaly Detection)
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18 pages, 6699 KiB  
Article
A Study on Partial Discharge Fault Identification in GIS Based on Swin Transformer-AFPN-LSTM Architecture
by Jiawei Li, Shangang Ma, Fubao Jin, Ruiting Zhao, Qiang Zhang and Jiawen Xie
Information 2025, 16(2), 110; https://doi.org/10.3390/info16020110 - 6 Feb 2025
Cited by 1 | Viewed by 710
Abstract
Aiming at the problem of manual feature extraction and insufficient mining of feature information for partial discharge pattern recognition under different insulation faults in GIS, a deep learning model based on phase and timing features with Swin Transformer-AFPN-LSTM architecture is proposed. Firstly, a [...] Read more.
Aiming at the problem of manual feature extraction and insufficient mining of feature information for partial discharge pattern recognition under different insulation faults in GIS, a deep learning model based on phase and timing features with Swin Transformer-AFPN-LSTM architecture is proposed. Firstly, a GIS insulation fault simulation experimental platform is constructed, and the PRPD phase data and TRPD timing data under different faults are obtained; secondly, the TRPD timing data are converted into MTF; then the PRPD phase data and MTF timing data are input into the Swin Transformer-AFPN-LSTM model and other deep learning models for performance comparison. The experimental results show that the Swin Transformer-AFPN-LSTM model improves the performance by 14.09–21.23% compared with the traditional CNN model and LSTM model. Moreover, using this model to extract phase features and timing features simultaneously improves the accuracy by 10.67% and 8.66%, respectively, compared with single feature extraction, and the overall accuracy reaches 98.82%, which provides a new idea for GIS insulation fault identification. Full article
(This article belongs to the Special Issue Emerging Research on Neural Networks and Anomaly Detection)
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13 pages, 1030 KiB  
Article
Anomalies Classification in Fan Systems Using Dual-Branch Neural Networks with Continuous Wavelet Transform Layers: An Experimental Study
by Cezary Pałczyński and Paweł Olejnik
Information 2025, 16(2), 71; https://doi.org/10.3390/info16020071 - 21 Jan 2025
Viewed by 779
Abstract
In this study, anomalies in a fan system were classified using a real measurement setup to simulate mechanical anomalies such as blade detachment or debris accumulation. Data were collected under normal operating conditions and with an added unbalancing mass. Additionally, sensor anomalies were [...] Read more.
In this study, anomalies in a fan system were classified using a real measurement setup to simulate mechanical anomalies such as blade detachment or debris accumulation. Data were collected under normal operating conditions and with an added unbalancing mass. Additionally, sensor anomalies were introduced by manipulating accelerometer readings and examining three types: spike, stuck, and dropout. To classify the anomalies, four neural network models—variations in Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) were tested. These models incorporated a Continuous Wavelet Transform (CWT) layer. A novel approach for implementing the CWT layer in both LSTM and CNN architectures was proposed, along with a dual-branch input structure featuring two CWT layers using different mother wavelets. The dual-branch configuration with different mother wavelets yielded better accuracy for the simpler LSTM network. Accuracy comparisons were conducted for the 10 best-performing models based on validation set predictions, revealing improved classification performance. The study concluded with a summary of prediction accuracy for both the validation and test sets of data, along with the calculation of average accuracy, demonstrating the effectiveness of the proposed dual-branch neural network structure in classifying anomalies in fan systems. Full article
(This article belongs to the Special Issue Emerging Research on Neural Networks and Anomaly Detection)
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21 pages, 12005 KiB  
Article
Shear Wave Velocity Prediction with Hyperparameter Optimization
by Gebrail Bekdaş, Yaren Aydın, Umit Işıkdağ, Sinan Melih Nigdeli, Dara Hajebi, Tae-Hyung Kim and Zong Woo Geem
Information 2025, 16(1), 60; https://doi.org/10.3390/info16010060 - 16 Jan 2025
Cited by 1 | Viewed by 953
Abstract
Shear wave velocity (Vs) is an important soil parameter to be known for earthquake-resistant structural design and an important parameter for determining the dynamic properties of soils such as modulus of elasticity and shear modulus. Different Vs measurement methods are [...] Read more.
Shear wave velocity (Vs) is an important soil parameter to be known for earthquake-resistant structural design and an important parameter for determining the dynamic properties of soils such as modulus of elasticity and shear modulus. Different Vs measurement methods are available. However, these methods, which are costly and labor intensive, have led to the search for new methods for determining the Vs. This study aims to predict shear wave velocity (Vs (m/s)) using depth (m), cone resistance (qc) (MPa), sleeve friction (fs) (kPa), pore water pressure (u2) (kPa), N, and unit weight (kN/m3). Since shear wave velocity varies with depth, regression studies were performed at depths up to 30 m in this study. The dataset used in this study is an open-source dataset, and the soil data are from the Taipei Basin. This dataset was extracted, and a 494-line dataset was created. In this study, using HyperNetExplorer 2024V1, Vs prediction based on depth (m), cone resistance (qc) (MPa), shell friction (fs), pore water pressure (u2) (kPa), N, and unit weight (kN/m3) values could be performed with satisfactory results (R2 = 0.78, MSE = 596.43). Satisfactory results were obtained in this study, in which Explainable Artificial Intelligence (XAI) models were also used. Full article
(This article belongs to the Special Issue Emerging Research on Neural Networks and Anomaly Detection)
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18 pages, 1873 KiB  
Article
Beyond Supervised: The Rise of Self-Supervised Learning in Autonomous Systems
by Hamed Taherdoost
Information 2024, 15(8), 491; https://doi.org/10.3390/info15080491 - 16 Aug 2024
Cited by 1 | Viewed by 3712
Abstract
Supervised learning has been the cornerstone of many successful medical imaging applications. However, its reliance on large labeled datasets poses significant challenges, especially in the medical domain, where data annotation is time-consuming and expensive. In response, self-supervised learning (SSL) has emerged as a [...] Read more.
Supervised learning has been the cornerstone of many successful medical imaging applications. However, its reliance on large labeled datasets poses significant challenges, especially in the medical domain, where data annotation is time-consuming and expensive. In response, self-supervised learning (SSL) has emerged as a promising alternative, leveraging unlabeled data to learn meaningful representations without explicit supervision. This paper provides a detailed overview of supervised learning and its limitations in medical imaging, underscoring the need for more efficient and scalable approaches. The study emphasizes the importance of the area under the curve (AUC) as a key evaluation metric in assessing SSL performance. The AUC offers a comprehensive measure of model performance across different operating points, which is crucial in medical applications, where false positives and negatives have significant consequences. Evaluating SSL methods based on the AUC allows for robust comparisons and ensures that models generalize well to real-world scenarios. This paper reviews recent advances in SSL for medical imaging, demonstrating their potential to revolutionize the field by mitigating challenges associated with supervised learning. Key results show that SSL techniques, by leveraging unlabeled data and optimizing performance metrics like the AUC, can significantly improve the diagnostic accuracy, scalability, and efficiency in medical image analysis. The findings highlight SSL’s capability to reduce the dependency on labeled datasets and present a path forward for more scalable and effective medical imaging solutions. Full article
(This article belongs to the Special Issue Emerging Research on Neural Networks and Anomaly Detection)
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16 pages, 5487 KiB  
Article
Rapid Forecasting of Cyber Events Using Machine Learning-Enabled Features
by Yussuf Ahmed, Muhammad Ajmal Azad and Taufiq Asyhari
Information 2024, 15(1), 36; https://doi.org/10.3390/info15010036 - 11 Jan 2024
Cited by 6 | Viewed by 4314
Abstract
In recent years, there has been a notable surge in both the complexity and volume of targeted cyber attacks, largely due to heightened vulnerabilities in widely adopted technologies. The Prediction and detection of early attacks are vital to mitigating potential risks from cyber [...] Read more.
In recent years, there has been a notable surge in both the complexity and volume of targeted cyber attacks, largely due to heightened vulnerabilities in widely adopted technologies. The Prediction and detection of early attacks are vital to mitigating potential risks from cyber attacks and network resilience. With the rapid increase of digital data and the increasing complexity of cyber attacks, big data has become a crucial tool for intrusion detection and forecasting. By leveraging the capabilities of unstructured big data, intrusion detection and forecasting systems can become more effective in detecting and preventing cyber attacks and anomalies. While some progress has been made on attack prediction, little attention has been given to forecasting cyber events based on time series and unstructured big data. In this research, we used the CSE-CIC-IDS2018 dataset, a comprehensive dataset containing several attacks on a realistic network. Then we used time-series forecasting techniques to construct time-series models with tuned parameters to assess the effectiveness of these techniques, which include Sequential Minimal Optimisation for regression (SMOreg), linear regression and Long Short-Term Memory (LSTM) to forecast the cyber events. We used machine learning algorithms such as Naive Bayes and random forest to evaluate the performance of the models. The best performance results of 90.4% were achieved with Support Vector Machine (SVM) and random forest. Additionally, Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) metrics were used to evaluate forecasted event performance. SMOreg’s forecasted events yielded the lowest MAE, while those from linear regression exhibited the lowest RMSE. This work is anticipated to contribute to effective cyber threat detection, aiming to reduce security breaches within critical infrastructure. Full article
(This article belongs to the Special Issue Emerging Research on Neural Networks and Anomaly Detection)
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17 pages, 1497 KiB  
Article
Decentralized Federated Learning-Enabled Relation Aggregation for Anomaly Detection
by Siyue Shuai, Zehao Hu, Bin Zhang, Hannan Bin Liaqat and Xiangjie Kong
Information 2023, 14(12), 647; https://doi.org/10.3390/info14120647 - 3 Dec 2023
Cited by 3 | Viewed by 2899
Abstract
Anomaly detection plays a crucial role in data security and risk management across various domains, such as financial insurance security, medical image recognition, and Internet of Things (IoT) device management. Researchers rely on machine learning to address potential threats in order to enhance [...] Read more.
Anomaly detection plays a crucial role in data security and risk management across various domains, such as financial insurance security, medical image recognition, and Internet of Things (IoT) device management. Researchers rely on machine learning to address potential threats in order to enhance data security. In the financial insurance industry, enterprises tend to leverage the relation mining capabilities of knowledge graph embedding (KGE) for anomaly detection. However, auto insurance fraud labeling strongly relies on manual labeling by experts. The efficiency and cost issues of labeling make auto insurance fraud detection still a small-sample detection challenge. Existing schemes, such as migration learning and data augmentation methods, are susceptible to local characteristics, leading to their poor generalization performance. To improve its generalization, the recently emerging Decentralized Federated Learning (DFL) framework provides new ideas for mining more frauds through the joint cooperation of companies. Based on DFL, we propose a federated framework named DFLR for relation embedding aggregation. This framework trains the private KGE of auto insurance companies on the client locally and dynamically selects servers for relation aggregation with the aim of privacy protection. Finally, we validate the effectiveness of our proposed DFLR on a real auto insurance dataset. And the results show that the cooperative approach provided by DFLR improves the client’s ability to detect auto insurance fraud compared to single client training. Full article
(This article belongs to the Special Issue Emerging Research on Neural Networks and Anomaly Detection)
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