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: 31 May 2024 | Viewed by 4066

Special Issue Editors


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Guest Editor
School of Cyber Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: network and system security; artificial intelligence; software engineering

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

Published Papers (2 papers)

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Research

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
Viewed by 1841
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 1 | Viewed by 1719
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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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Optimize Face Detection by Incorporating Classification Networks to Minimize False Positive Rates
Authors: Hui Cui
Affiliation: Monash University, Melbourne, Australia
Abstract: The rise of convolutional neural networks (CNNs) has significantly progressed face detection, notably improving accuracy and recall metrics. Precision and recall are crucial in assessing CNN-based detection models, yet a common tendency is to prioritize true positive rates while over looking false positives. A key factor contributing to this imbalance is the absence of pseudo-face images in training and evaluation datasets. This shortfall undermines the regression capabilities of detection models, resulting in numerous incorrect detections and subpar localization. To address this gap, we introduce the WIDERFACE dataset, which incorporates a substantial proportion of pseudo-face images synthesized by merging human and animal facial features. This dataset is specifically designed to enhance false positive detection in training scenarios. Additionally, we present a novel face detection framework that integrates a classification model into the existing face detection model to reduce the false positive rate and improve detection accuracy. Comparative evaluations on the WIDERFACE and other popular datasets demonstrate that our framework achieves a reduced false positive rate while maintaining the true positive rate relative to existing leading face detection models.

Title: Multi-identity Recognition of Darknet Vendors Based on Metric Learning
Authors: Yilei WANG; Xin SUN; Jiajia HAN; Yuelin Hu; Wenliang Xu; Futai Zou
Affiliation: School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
Abstract: Dark web vendor identification can be seen as an authorship aliasing problem, aiming to determine whether different accounts on different markets belong to the same real-world vendor, in order to locate cybercriminals involved in dark web market transactions. Existing open-source datasets for dark web marketplaces are outdated and cannot simulate real-world situations, while data labeling methods are difficult and suffer from issues such as inaccurate labeling and limited cross-market research. The problem of identifying vendors’ multiple identities on the dark web involves a large number of categories and a limited number of samples, making it difficult to use traditional multiclass classification models. To address these issues, this paper proposes a metric learning-based method for dark web vendor identification, collecting product data from 21 currently active English dark web marketplaces and using a multi-dimensional feature extraction method based on product titles, descriptions, and images. Using pseudo-labeling technology combined with manual labeling improves data labeling accuracy compared to previous labeling methods. The proposed method uses a Siamese neural network with metric learning to learn the similarity between vendors and achieve the recognition of vendors’ multiple identities. This method achieved better performance with an average F1-Score of 0.889 and an accuracy rate of 97.54% on the constructed dataset. The contributions of this paper lie in the proposed method for collecting and labeling data for dark web marketplaces and overcoming the limitations of traditional multiclass classifiers to achieve effective recognition of vendors’ multiple identities.

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