Anomaly Detection - Science topic

The rapid integration of smart grids into modern electrical infrastructure has raised concerns about their security and the protection of sensitive data. In this context, anomaly detection plays a critical role in identifying irregularities or malicious activities within smart grid systems. However, the conventional approaches to anomaly detection...

Special Issue Information Dear Colleagues, The fields of Machine Learning (ML), Deep Learning (DL), and Artificial Intelligence (AI) have undergone remarkable expansions over the past few decades. This surge in growth can be largely attributed to significant advancements in computing power and the unprecedented availability of vast amounts of data...

Neurological diseases, such as multiple sclerosis (MS), significantly affect hand function, impacting patients' independence and quality of life. The Nine Hole Peg Test (NHPT) is a standardized tool widely used to assess upper limb motor function. This paper explores the integration of artificial intelligence (AI) and machine learning (ML) in the a...

The rapid evolution of financial technology (FinTech) has transformed global financial services, offering convenience and accessibility. However, this digital shift has also led to a surge in sophisticated fraud attempts, necessitating advanced security measures. Machine learning (ML) has emerged as a crucial tool in fraud detection, enabling finan...

Software-Defined Networking (SDN) offers significant advantages for modern networks, including flexibility, centralized control, and reduced dependency on vendor-specific hardware. However, these benefits introduce security vulnerabilities, particularly from Distributed Denial-of-Service (DDoS) attacks, which represent some of the most disruptive t...

Detecting money laundering within financial networks is challenging due to the complexity of illicit transactions and the scarcity of labeled data. In this study, we model accounts as nodes and transactions as edges to develop an un-supervised anomaly detection framework, AMLGaurd, utilizing Graph Auto-Encoders (GAEs). GAEs encode the structural an...

The advent of machine learning (ML) has revolutionized healthcare, particularly in critical care and patient monitoring systems. This study explores the integration of ML for real-time big data analytics to enhance patient outcomes in intensive care units (ICUs). It presents a synthesis of recent advancements, highlighting the benefits of predictiv...

In this review, we propose a cybersecurity framework aimed at enhancing fraud detection in financial systems by leveraging artificial intelligence (AI), microservices, and RESTful architectures. With the increasing sophistication of cyber threats targeting financial institutions, traditional security methods often fall short in providing comprehens...

Visual inspections using the traditional method can be inefficient and complicated in some buildings, making the anomaly survey process slow and inaccurate. Recently, studies have been carried out on digital surveying and reality capture techniques as a way of inspecting and capturing the in-situ state of structures, especially in complex scenarios...

Blockchain technology has emerged as a transformative tool in the fintech industry, offering unprecedented opportunities to enhance real-time auditing processes, financial transparency, and fraud detection. This study explores how the integration of blockchain with advanced analytics can revolutionize traditional auditing practices, addressing crit...

Anomaly detection in healthcare data is a crucial aspect of ensuring data quality, identifying unusual health conditions, and enhancing patient outcomes. Healthcare data is inherently complex, comprising diverse formats like medical records, imaging, and real-time monitoring data. This complexity, coupled with the sensitivity of medical information...

Aim: The study investigates how deep learning, particularly YOLOv4, might be included into the CCTV systems of the Federal Polytechnic Ile-Oluji Library for proactive monitoring, real-time anomaly detection, and resource optimization. Selected for real-time surveillance in dynamic environments, YOLOv4 balances speed and accuracy against alternative...

The rising number of insider threats poses a significant challenge to modern organizations, with data breaches often resulting from anomalous access behavior by trusted individuals. Advanced Digital Signal Processing and Modulation (DSPM) techniques offer a promising solution to enhance the detection of insider threats by closely monitoring data ac...

Anomaly detection is crucial in time series analysis for identifying abnormal events. To address the limitations of traditional methods in integrating spatiotemporal correlations and modeling normal patterns, we propose a Time Series Anomaly Detection Model Based on Spatio-Temporal Feature Fusion (TADST). First, the Spatio-Temporal Feature Fusion N...

Clustering is a fundamental unsupervised task in machine learning. It involves grouping a set of objects into distinct clusters based on their inherent properties. Clustering finds applications in various domains, such as image segmentation, customer segmentation, document categorization, anomaly detection, and social network analysis. In this pape...

Video anomaly detection has emerged as an important research topic in computer vision and analysis. This paper presents an improved spatio-temporal color mechanism for video anomaly detection, incorporating adaptive color modules and multi-scale feature enhancement techniques. The proposed system uses a two-stream architecture that processes spatia...

Demand forecasting has emerged as a crucial element in supply chain management. It is essential to identify anomalous data and continuously improve the forecasting model with new data. However, existing literature fails to comprehensively cover both aspects of anomaly detection and continuous improvement in demand forecasting. This study proposes a...

Automating financial document processing is a key priority in the financial sector to improve efficiency, reduce manual work, and increase accuracy. Instead, this paper proposes a whole process system that integrates OCR, NLP, and DL technologies for the efficient flow of documents. By integrating advanced preprocessing techniques, robust OCR model...

At present, artificial intelligence has emerged as a significant field all over the world, offering diverse enhancements across many industries when combined with it. This study primarily examines the combination of the Internet of Things and artificial intelligence, highlighting practical applications and areas requiring enhancement. Mainly from a...

This study presents a Generative AI-Enhanced Cybersecurity Framework designed to strengthen enterprise data privacy management while improving threat detection accuracy and scalability. By leveraging Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and traditional anomaly detection methods, the framework generates synthetic...

Gravitational waves carry information about far away phenomena which astronomers can use to form a better picture of the universe. Over the past few years, numerous gravitational waves have been detected using a method called matched filtering; however, this approach has two main drawbacks. First, it requires knowledge of the sought-after waveform...

Anomaly detection is crucial in the energy sector to identify irregular patterns indicating equipment failures, energy theft, or other issues. Machine learning techniques for anomaly detection have achieved great success, but are typically centralized, involving sharing local data with a central server which raises privacy and security concerns. Fe...

Data Security Posture Management (DSPM) has become a cornerstone in modern cybersecurity strategies, providing organizations with comprehensive visibility and control over their data assets. This paper explores the use of DSPM-driven anomaly detection techniques to enhance the detection and prevention of insider threats, focusing specifically on mo...

A newly proposed hybrid approach that makes use of both supervised and unsu pervised machine learning to implement security within blockchain transactions. Blockchain, despite its central role in the decentralized networks and the crypto graphic cryptography, is still open to high-end attacks. Making use of random forest, autoencoders, and SVM mode...

Anomaly detection (AD) is an important topic in remote sensing, aiming to identify unusual or abnormal features within the data. However, most existing low-rank representation methods usually use the nuclear norm for background estimation, and do not consider the different contributions of different singular values. Besides, they overlook the spati...

The pharmaceutical industry faces increasing pressure to enhance efficiency, reduce costs, and accelerate innovation while ensuring regulatory compliance. This study explores the integration of Intelligent Process Automation (IPA) and Generative AI to optimize pharmaceutical manufacturing, drug discovery, and supply chain management. By leveraging...

This comprehensive article explores the transformative role of machine learning in modern semiconductor design verification, focusing on functional coverage closure challenges and solutions. The article examines how ML technologies have revolutionized traditional verification methodologies, particularly in handling complex Leveraging Machine Learni...

This article presents an innovative approach to automating firewall policy management through the integration of artificial intelligence (AI) and microservices architecture. The proposed article framework addresses critical challenges in traditional firewall management, including rule redundancy, configuration inconsistencies, and human error. By l...

The increasing complexity of modern software development has led to the rise of DevSecOps, an approach that integrates security into the DevOps pipeline. However, traditional security measures often struggle to keep pace with the rapid deployment cycles and evolving threats. Artificial intelligence (AI) has emerged as a transformative solution for...

Artificial Intelligence (AI) and Machine Learning (ML) are transforming leak detection in underground pipelines, offering enhanced accuracy, faster detection, and predictive capabilities that surpass traditional methods. Conventional leak detection techniques often suffer from high false positives, slow response times, and limitations in detecting...

An effective neural network system for monitoring sensors in helicopter turboshaft engines has been developed based on a hybrid architecture combining LSTM and GRU. This system enables sequential data processing while ensuring high accuracy in anomaly detection. Using recurrent layers (LSTM/GRU) is critical for dependencies among data time series a...

This study presents a novel approach for anomaly event detection in large-scale civil structures by integrating transfer learning (TL) techniques with extended node strength network analysis based on video data. By leveraging TL with BEiT + UPerNet pretrained models, the method identifies structural Region-of-Uninterest (RoU), such as windows and d...

In the wake of devastating cyclones, rapid and accurate assessment of crop damage is crucial for timely intervention and resource allocation. The acquiring of high-quality and up-to-date satellite or aerial imagery immediately following a cyclone is often difficult due to adverse weather conditions and limited access to affected areas. The objectiv...

The rapid evolution of cyber threats and the increasing complexity of IT infrastructures have highlighted the critical need for more robust cybersecurity measures within IT governance frameworks. Traditional cybersecurity audits, while effective in identifying vulnerabilities and ensuring compliance, often fall short in addressing dynamic threat la...

Blazars are a subclass of active galactic nuclei with relativistic jets pointing toward the observer. They are notable for their flux variability at all observed wavelengths and timescales. Together with simultaneous measurements at lower energies, the very-high-energy (VHE) emission observed during blazar flares may be used to probe the population...

This study proposes a system that evaluates the quality of espresso crema in real time using the deep learning-based anomaly detection model, f-AnoGAN. The system integrates mobile devices to collect sensor data during the extraction process, enabling quick adjustments for optimal results. Using the GrabCut algorithm to separate crema from the back...

Cybersecurity threats are evolving, necessitating advanced detection mechanisms. This paper proposes an innovative framework integrating Eigenvalue Matrix Analysis, Game Theory, and Artificial Intelligence (AI) to detect and mitigate the "Flamingo" attack-a hypothetical advanced cyber threat. We employ eigenvalue decomposition to analyze network tr...

Unsupervised machine learning methods are well suited to searching for anomalies at scale but can struggle with the high-dimensional representation of many modern datasets, hence dimensionality reduction (DR) is often performed first. In this paper we analyse unsupervised anomaly detection (AD) from the perspective of the manifold created in DR. We...

Existing methods for detecting anomalies in digital light processing (DLP) 3D printing and performing in-situ repairs can reduce most defects and improve success rates. However, since printing control parameters cannot adapt to real-time printing conditions, anomalies may persist across successive layers, and continuous repairs could ultimately lea...

Critical National Infrastructure includes large networks such as telecommunications, transportation, health services, police, nuclear power plants, and utilities like clean water, gas, and electricity. The protection of these infrastructures is crucial, as nations depend on their operation and stability. However, cyberattacks on such systems appear...

This paper addresses the critical challenge of understanding and interpreting deep learning models in Global Navigation Satellite System (GNSS) applications, specifically focusing on multipath effect detection and analysis. As GNSS systems become increasingly reliant on deep learning for signal processing, the lack of model interpretability poses s...

This paper examines various data analytics techniques, including predictive modeling, anomaly detection, and big data analytics, that help identify suspicious transactions. The study also reviews regulatory and ethical considerations in financial fraud detection. Future research directions in AI-powered analytics for fraud prevention are also highl...

Wireless sensor networks (WSNs) play a critical role in applications such as wildlife monitoring, disaster recovery, and precision agriculture, where continuous coverage and longevity are paramount amidst dynamic environmental challenges. To address these demands, the cellular adaptive energy forecasting and coverage optimization (CAEFCO) framework...

The detection, description and understanding of anomalies in multivariate time series data is an important task in several industrial domains. Automated data analysis provides many tools and algorithms to detect anomalies, while visual interfaces enable domain experts to explore and analyze data interactively to gain insights using their expertise....

Regarding the transportation of people, commodities, and other items, aeroplanes are an essential need for society. Despite the generally low danger associated with various modes of transportation, some accidents may occur. The creation of a machine learning model employing data from autonomous-reliant surveillance transmissions is essential for th...

In this paper, we present a tool for analyzing .NET CLR event logs based on a novel method inspired by Natural Language Processing (NLP) approach. Our research addresses the growing need for effective monitoring and optimization of software systems through detailed event log analysis. We utilize a BERT-based architecture with an enhanced tokenizati...

Anomaly detection based on 3D point cloud data is an important research problem and receives more and more attention recently. Untrained anomaly detection based on only one sample is an emerging research problem motivated by real manufacturing industries such as personalized manufacturing that only one sample can be collected without any additional...

The increasing complexity of cryptographic extortion techniques has necessitated the development of adaptive detection frameworks capable of identifying adversarial encryption behaviors without reliance on predefined signatures. Hierarchical Entropic Diffusion (HED) introduces a structured entropy-based anomaly classification mechanism that systema...

An innovative analog frontend for big data collection and intelligent compression as part of an instantaneous failure prediction platform is presented. Failure prediction in power management systems is crucial for increasing uptime and preventing massive failure. Accurate failure prediction, with real-time decision-making, requires data collection...

Machine-learning applications are becoming increasingly widespread. However, machine learning is highly dependent on high-quality, large-scale training data. Due to the limitations of data privacy and security, in order to accept more user data, users are required to participate in the computation themselves through the secure use of secret keys. I...

Anomaly detection plays a vital role for e-commerce businesses due to the enormous volume of numeric data they handle. Price data is a prominent example of such data, and detecting anomalies in numeric data is challenging due to potential errors and outliers, which can disrupt sales calculations and result in financial losses. Anomaly detection in...

This paper investigates unsupervised anomaly detection in multivariate time-series data using reinforcement learning (RL) in the latent space of an autoencoder. A significant challenge is the limited availability of anomalous data, often leading to misclassifying anomalies as normal events, thus raising false negatives. RL can help overcome this li...

Congenital disorders of glycosylation (CDG) constitute a group of rare inherited metabolic disorders resulting from mutations in genes involved in the biosynthesis of glycan chains that are covalently attached to proteins or lipids. To date, nearly 200 genes have been identified as responsible for these disorders, with approximately half implicated...

The complexity and openness of railway turnout environments pose great challenges to anomaly detection, and supervised methods are highly dependent on labels, making it difficult to address the diverse types of anomalies and the scarcity of samples in turnout environments. To solve these problems, this paper proposes a new method, Rail-PatchCore, w...

Effective anomaly detection in complex systems requires identifying change points (CPs) in the frequency domain, as abnormalities often arise across multiple frequencies. This paper extends recent advancements in statistically significant CP detection, based on Selective Inference (SI), to the frequency domain. The proposed SI method quantifies the...

As healthcare systems increasingly adopt fog computing to improve responsiveness and real-time data processing at the edge, significant security challenges emerge due to the decentralized architecture. The traditional perimeter-based security models are inadequate for addressing the dynamic and distributed nature of fog networks, leaving them vulne...

This paper proposes an enhanced contrastive ensemble learning method for anomaly sound detection. The proposed method achieves approximately 6% in the AUC metric in some categories and achieves state-of-the-art performance among self-supervised models on multiple benchmark datasets. The proposed method is effective in automatically monitoring the o...

Anomaly detection is a crucial task in computer vision, with applications ranging from quality control to security monitoring, among many others. Recent technological advancements have enabled near‐perfect solutions on benchmark datasets like MVTec, raising the need for novel datasets that pose new challenges for this modelling task. This work pres...

This article presents a detailed exploration of functional safety semiconductor chip design for automotive applications with Functional safety and cyber security capability, focusing on Software-Defined Vehicle (SDV) architectures. The integration of AI/ML accelerators in semiconductor chips is examined to enhance real-time performance, safety comp...

Lithium-ion batteries are a key technology in supply chains for modern electric vehicles. Their production is complex and can be prone to defects. As such, the detection of defective batteries is critical to ensure performance and consumer safety. Existing end-of-line testing relies heavily on electrical measurements for identifying defective cells...

Cold spray (CS) is an emerging additive manufacturing method used to deposit a wide range of materials by spraying solid particles at supersonic velocities using high-pressure millimeter scale de Laval nozzles. As CS technology finds applications in diverse areas, including 3D printing, the need for in situ process monitoring becomes increasingly a...

In military operations, real-time monitoring of soldiers’ health is essential for ensuring mission success and safeguarding personnel, yet such systems face challenges related to accuracy, security, and resource efficiency. This research addresses the critical need for secure, real-time monitoring of soldier vitals in the field, where operational s...

Long-term operation of proton exchange membrane (PEM) fuel cells requires precise monitoring to detect and prevent harmful anomalies. Cell voltage is a sensitive indicator for the fuel cell’s state of health. However, its dependency on fuel cell operating conditions impedes the detection of anomalies. To overcome this limitation, we present a machi...

Anomaly detection is vital for enhancing the safety of Industrial Control Systems (ICS). However, the complicated structure of ICS creates complex temporal correlations among devices with many parameters. Current methods often ignore these correlations and poorly select parameters, missing valuable insights. Additionally, they lack interpretability...

With the rapid growth of data volumes in real-world applications, anomaly detection has become a crucial task across various scenarios. Anomalies are generally defined as data points that constitute a small proportion yet exhibit significantly different patterns. Numerous detection methods have been proposed and applied, ranging from statistical an...

The rapid adoption of digital payment systems has revolutionized financial transactions, but it has also introduced significant challenges in combating fraud. Traditional rule-based fraud detection methods are increasingly inadequate against sophisticated and evolving fraud schemes. This research explores the transformative impact of machine learni...

Advanced persistent threats (APTs) are sophisticated cyber attacks that can remain undetected for extended periods, making their mitigation particularly challenging. Given their persistence, significant effort is required to detect them and respond effectively. Existing provenance-based attack detection methods often lack interpretability and suffe...

Detecting defects in photovoltaic cells is essential for maintaining the reliability and efficiency of solar power systems. Existing methods face challenges such as (1) the interaction and fusion of features at different layers in the feature extraction network of the object detection model are not sufficient, (2) ineffective detection of uniquely...

Finding dense subgraphs is a core problem with numerous graph mining applications such as community detection in social networks and anomaly detection. However, in many real-world networks connections are not equal. One way to label edges as either strong or weak is to use strong triadic closure~(STC). Here, if one node connects strongly with two o...

Traditional Anomaly Detection (AD) methods have predominantly relied on unsupervised learning from extensive normal data. Recent AD methods have evolved with the advent of large pre-trained vision-language models, enhancing few-shot anomaly detection capabilities. However, these latest AD methods still exhibit limitations in accuracy improvement. O...

When cyber–physical systems (CPSs) are connected to the Internet or other CPSs with connectivity, external adversaries can potentially gain access to the CPS and attempt to control the electronic control units (ECUs). In particular, the lack of confidentiality and integrity in the controller area networks (CANs) of CPSs makes it difficult to distin...

This paper introduces a new probabilistic composite model for the detection of zero-day exploits targeting the capabilities of existing anomaly detection systems in terms of accuracy, computational time, and adaptability. To address the issues mentioned above, the proposed framework consisted of three novel elements. The first key innovations are t...

As cloud computing continues to dominate the digital landscape, its growing adoption introduces new challenges in cybersecurity, particularly concerning advanced persistent threats (APTs) and sophisticated attack vectors. This paper explores the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing cloud securit...

Acoustic telemetry data plays a vital role in understanding the behaviour and movement of aquatic animals. However, these datasets, which often consist of millions of individual data points, frequently contain anomalous movements that pose significant challenges. Traditionally, anomalous movements are identified either manually or through basic sta...