In the civil aviation industry, security risk management has shifted from post-accident investigations and analyses to pre-accident warnings in an attempt to reduce flight risks by identifying currently untracked flight events and their trends and ef...
Effective anomaly detection is essential for realizing modern and secure industrial control systems. However, the direct integration of anomaly detection within such a system is complex due to the wide variety of hardware used, different communicatio...
Machine learning (ML) has achieved remarkable success in a wide range of applications. In recent ML research, deep anomaly detection (AD) has been a hot topic with the aim of discriminating among anomalous data with deep neural networks (DNNs). Notab...
Modern user authentication systems increasingly need user and device-behavior-aware adaptive mechanisms to detect evolving threats beyond the traditional authentication framework of static credential verification. This paper proposes a hybrid multi-m...
With the increasing scale of industrial devices and the growing complexity of multi-source heterogeneous sensor data, traditional methods struggle to address challenges in fault detection, data security, and trustworthiness. Ensuring tamper-proof dat...
Signalling traffic in 5G Interconnect and Roaming Networks (IRNs) over the IP eXchange (IPX) faces man-in-the-middle (MITM) interception on N32 and GTP-U, billing fraud, and “Harvest Now, Decrypt Later” (HNDL) adversaries. This paper deve...
Convolutional Neural Networks (CNNs) are fundamental tools for image analysis and recognition in monitoring systems, particularly in photovoltaic (PV) installations, where visual inspection and thermal imaging play crucial roles in anomaly detection...
Industrial Internet of Things (IIoT) deployments face a security challenge that neither physics-based nor AI-based anomaly detection addresses alone: physics models are adversarially robust but miss behavioral attacks that remain within physical boun...
The adoption and use of the Internet of Things (IoT) have increased rapidly over recent years, and cyber threats in IoT devices have also become more common. Thus, the development of a system that can effectively identify malicious attacks and reduce...
This study introduces the Hybrid Integrated Prediction-Error Reconstruction-based Anomaly Detection (HIPER-CHAD) model, which addresses the challenge of reliably detecting subtle anomalies in noisy multivariate indoor environmental time-series data....
Transmission mounted electric drive type hybrid electric vehicles (HEVs) engage/disengage an engine clutch when EV↔HEV mode transitions occur. If this engine clutch is not adequately engaged or disengaged, driving power is not transmitted correctly....
The Gaizi River Basin, an alpine region in China crossed by the Karakoram Highway, is highly prone to glacier-related debris flows (GDF). Accurate debris flow susceptibility assessment in this high-altitude area remains challenging due to complex ter...
Unsupervised anomaly detection has been extensively studied. However, most existing methods are designed for either numerical or nominal data, which struggle to detect anomalies effectively in real-world mixed-type datasets. Fuzzy information granula...
With an increasing number of Internet of Things (IoT) devices in the digital world, the attack surface for consumer networks has been increasing exponentially. Most of the compromised devices are used as zombies for attacks such as Distributed Denial...
Key performance indicator (KPI) anomaly detection is the underlying core technology in Artificial Intelligence for IT operations (AIOps). It has an important impact on subsequent anomaly location and root cause analysis. Variational auto-encoder (VAE...
Multivariate time-series data with a contextual spatial attribute have extensive use for finding anomalous patterns in a wide variety of application domains such as earth science, hurricane tracking, fraud, and disease outbreak detection. In most set...
The proliferation of Internet of Medical Things (IoMT) devices connected to the internet poses significant challenges to data integrity, confidentiality, and patient safety due to their vulnerability to outside exploitation. Specifically, IoMT device...
Security monitoring in Industrial Internet of Things environments requires telemetry that spans Information Technology (IT) and Operational Technology (OT) network layers, and most public datasets capture only one such view. We describe a design patt...
To address challenges in stock price prediction including data nonlinearity and anomalies, we propose a hybrid CNN-BiGRU-AM framework integrated with deep learning-based anomaly detection. First, an anomaly detection module identifies irregularities...
Due to connectivity and automation trends, the medical device industry is experiencing increased demand for safety and security mechanisms. Anomaly detection has proven to be a valuable approach for ensuring safety and security in other industries, s...
Non-Intrusive Load Monitoring (NILM) enables estimation of the energy use of individual appliances in smart buildings from a single aggregate meter. In practice, however, this task is not straightforward. Signals from different appliances can overlap...
The development of sensor networks allows for easier time series data acquisition in industrial production. Due to the redundancy and rapidity of industrial time series data, accurate anomaly detection is a complex and important problem for the effic...
This paper investigates the use of an unsupervised hybrid statistical–local outlier factor algorithm to detect anomalies in time-series flight data. Flight data analysis is an activity carried out by airlines primarily as a means of improving t...
With the rapid development in technology in recent years, the use of cameras and the production of video and image data have similarly increased. Therefore, there is a great need to develop and improve video surveillance techniques to their maximum e...
Modern network infrastructures are under attack from increasingly sophisticated attacks that static, rule-based defenses cannot adequately mitigate. We introduce a multilayer anomaly detection and mitigation framework for Software-Defined Networking...
Wearable sleep monitoring systems provide a scalable and low-burden alternative to laboratory-based polysomnography, but overnight physiological recordings collected from wearable sensors are frequently corrupted by poor skin contact, sensor displace...
Deep groundwater dynamics, influenced by multiple factors, exhibit multi-scale periodicity, nonlinearity, and non-stationarity, posing significant challenges for reliable groundwater level prediction and anomaly detection. To address this issue, this...
Internet of Things (IoT) devices have revolutionized real-time monitoring and distant patient care in smart cities’ healthcare systems. However, this advancement has come with several issues, such as data security, scalability, operational effi...
Industrial defect detection in edge computing environments faces critical challenges in balancing accuracy, efficiency, and adaptability under data scarcity. To address these limitations, we propose the Hybrid Anomaly Detection System (HyADS), a nove...
Objectives: This study aims to present an AI-driven threat-hunting framework that automates both hypothesis generation and hypothesis validation through a hybrid deep learning model that combines Convolutional Neural Networks (CNN) with Long Short-Te...
To improve the accuracy and stability of anomaly detection in lithium-ion batteries for electric bicycles, in this study, we propose a hybrid deep learning model that integrates a convolutional neural network (CNN), long short-term memory (LSTM) netw...
Rule-based inspection of web server logs cannot detect attacks it has not already been told to look for, and most anomaly-detection studies validate their methods on a single dataset, which risks overstating how well a method generalises to new traff...
The rapid surge of Artificial Internet-of-Things (AIoT) devices has outpaced the deployment of robust, privacy-preserving anomaly detection solutions suitable for resource-constrained edge environments. This paper presents a two-stage hybrid Federate...
It is often difficult to describe natural time series due to implicit dependences and correlated noise. During anomalous natural processes, anomalous features appear in data. They have a nonstationary structure and do not allow us to apply traditiona...
Medical anomaly detection is challenged by limited labeled data and domain shifts, which reduce the performance and generalization of deep learning (DL) models. Hybrid convolutional neural network–Vision Transformer (CNN–ViT) architecture...
The rapid evolution of 5G and emerging 6G networks has increased system complexity, data volume, and security risks, making anomaly detection vital for ensuring reliability and resilience. However, existing machine learning (ML)-based approaches stil...
Distributed databases are increasingly used in enterprise and cloud environments, but their distributed architecture introduces significant security challenges, including data leaks and insider threats. In the context of escalating cyber threats targ...
The rapid expansion of the Internet of Things (IoT) has introduced new vulnerabilities that traditional security mechanisms often fail to address effectively. Signature-based intrusion detection systems cannot adapt to zero-day attacks, while rule-ba...
Maritime refrigerated containers operate under harsh and highly variable conditions, where gradual equipment degradation can lead to temperature excursions, cargo losses, and operational disruptions. In current practice, monitoring relies largely on...
Recently, artificial intelligence (AI) techniques have been used to describe the characteristics of information, as they help in the process of data mining (DM) to analyze data and reveal rules and patterns. In DM, anomaly detection is an important a...
This study compares deep learning models for point-level anomaly detection in multichannel satellite telemetry data. Raw event-based telemetry was converted into segment-based multivariate time series without windowing or feature extraction, allowing...
Internet of Things (IoT) environments, which are distributed and resource-constrained, present unique security challenges, making it essential to develop robust and transparent intrusion detection system (IDS) solutions. This study presents a special...
IoT environments have introduced diverse logistic support services into our lives and communities, in areas such as education, medicine, transportation, and agriculture. However, with new technologies and services, the issue of privacy and data secur...
Wireless Sensor Networks (WSNs) play a critical role in environmental monitoring and early forest fire detection. However, they are susceptible to sensor malfunctions and network intrusions, which can compromise data integrity and lead to false alarm...
Moroccan installations of solar photovoltaic panels experience operational difficulties due to shading and vegetation-related soiling, which reduce energy output by 15–30%. Most monitoring systems depend upon a single vegetation index, which ca...
Invariant-rule-based anomaly detection is attractive for industrial control systems (ICSs) because its rules are interpretable, auditable, and learnable from normal-operation data alone. However, mined invariants can miss attacks that induce weak, lo...
The adoption of Internet of Things (IoT) technologies has significantly enhanced precision farming by enabling continuous environmental monitoring and data-driven agricultural management. However, the increasing reliance on distributed sensor network...
Intrusion detection systems face challenges in processing high-volume network traffic while maintaining accuracy across diverse low volume attack types. This study presents a hybrid approach combining ARIMA time series forecasting with Decision Tree...
Unmanned Aerial Vehicles (UAVs) are increasingly being used in critical infrastructure, defense, and civilian applications, and face new cybersecurity threats. In this work, we present a novel hybrid deep learning architecture that combines Mamba, Ko...
Lithium-ion batteries in hybrid solar-grid systems experience complex electro-thermal dynamics and stochastic mode switching that threshold-based battery management systems fail to capture. This paper proposes a hybrid deviation detection framework t...
of 11