Mechanical defects in real situations affect observation values and cause abnormalities in multivariate time series, such as sensor values or network data. To perceive abnormalities in such data, it is crucial to understand the temporal context and interrelation between variables simultaneously. The anomaly detection task for time series, especially for unlabeled data, has been a challenging problem, and we address it by applying a suitable data degradation scheme to self-supervised model training. We define four types of synthetic outliers and propose the degradation scheme in which a portion of input data is replaced with one of the synthetic outliers. Inspired by the self-attention mechanism, we design a Transformer-based architecture to recognize the temporal context and detect unnatural sequences with high efficiency. Our model converts multivariate data points into temporal representations with relative position bias and yields anomaly scores from these representations. Our method, AnomalyBERT, shows a great capability of detecting anomalies contained in complex time series and surpasses previous state-of-the-art methods on five real-world benchmarks. Our code is available at https://github.com/Jhryu30/AnomalyBERT.
Deep Learning for Network Anomaly Detection under Data Contamination: Evaluating Robustness and Mitigating Performance Degradation
Groupe de Recherche Interdisciplinaire en Cybersécurité (GRIC)（跨学科网络安全研究组（GRIC））；Department of Computer Science, Université de Sherbrooke（谢布鲁克大学计算机科学系）；Department of Management School, Université de Sherbrooke（谢布鲁克大学管理学院）
Automating the Deep Space Network Data Systems; A Case Study in Adaptive Anomaly Detection through Agentic AI
Electrical and Computer Engineering Department, University of California San Diego（加州大学圣地亚哥分校电气与计算机工程系）；Department of Natural Sciences, Pasadena City College（帕萨迪纳城市学院自然科学系）；Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）
RobustMAD: Evaluating Real-World Robustness of Multimodal Small Language Models for Deployable Anomaly Detection Assistants
Singapore University of Technology and Design（新加坡科技设计大学）；Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局）；Nanyang Technological University（南洋理工大学）；Chongqing University（重庆大学）
BenchReAD: A systematic benchmark for retinal anomaly detection
The Center for Smart Health, School of Nursing, the Hong Kong Polytechnic University, Hong Kong, China（香港理工大学护理学院智能健康中心）；School of Biomedical Engineering, Tsinghua University, Beijing, China（清华大学生物医学工程学院）；Research Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China（中国科学院深圳先进技术研究院医学人工智能研究中心）
A fundamental problem in the field of unsupervised machine learning is the detection of anomalies corresponding to rare and unusual observations of interest; reasons include for their rejection, accommodation or further investigation. Anomalies are intuitively understood to be something unusual or inconsistent, whose occurrence sparks immediate attention. More formally anomalies are those observations-under appropriate random variable modelling-whose expectation of occurrence with respect to a grouping of prior interest is less than one; such a definition and understanding has been used to dev…
Online Conformal Anomaly Detection with Prediction-Powered Data Acquisition
King’s Communications, Learning & Information Processing (KCLIP) lab within the Centre for Intelligent Information Processing Systems (CIIPS), Department of Engineering, King’s College London（伦敦国王学院工程系智能信息处理系统中心（CIIPS）通信、学习与信息处理实验室（KCLIP））
CCAD: Compressed Global Feature Conditioned Anomaly Detection
Columbia University（哥伦比亚大学）；Ping An Property & Casualty Insurance Company（平安财产保险股份有限公司）；University of Michigan（密歇根大学）；University of California at Berkeley（加州大学伯克利分校）；Stevens Institute of Technology（史蒂文斯理工学院）；VisionX LLC.（VisionX有限责任公司）
ADVA Optical Networking SE（ADVA光网络公司）；Kiel University (CAU)（基尔大学（克里斯蒂安-阿尔布雷希茨大学））
Semiconductor lasers, one of the key components for optical communication systems, have been rapidly evolving to meet the requirements of next generation optical networks with respect to high speed, low power consumption, small form factor etc. However, these demands have brought severe challenges to the semiconductor laser reliability. Therefore, a great deal of attention has been devoted to improving it and thereby ensuring reliable transmission. In this paper, a predictive maintenance framework using machine learning techniques is proposed for real-time heath monitoring and prognosis of sem…
PatchAD: A Lightweight Patch-based MLP-Mixer for Time Series Anomaly Detection
School of Future Technology, South China University of Technology（未来技术学院，华南理工大学）；Pengcheng Laboratory（鹏城实验室）；School of Computer Science and Engineering, South China University of Technology（计算机科学与工程学院，华南理工大学）
As the number of Internet of Things (IoT) devices and systems have surged, IoT data analytics techniques have been developed to detect malicious cyber-attacks and secure IoT systems; however, concept drift issues often occur in IoT data analytics, as IoT data is often dynamic data streams that change over time, causing model degradation and attack detection failure. This is because traditional data analytics models are static models that cannot adapt to data distribution changes. In this paper, we propose a Performance Weighted Probability Averaging Ensemble (PWPAE) framework for drift adaptiv…
Time series anomaly detection (TSAD) is an important data mining task with numerous applications in the IoT era. In recent years, a large number of deep neural network-based methods have been proposed, demonstrating significantly better performance than conventional methods on addressing challenging TSAD problems in a variety of areas. Nevertheless, these deep TSAD methods typically rely on a clean training dataset that is not polluted by anomalies to learn the "normal profile" of the underlying dynamics. This requirement is nontrivial since a clean dataset can hardly be provided in practice. …
Vertilas GmbH（Vertilas有限公司）；ADVA Optical Networking SE（ADVA光网络股份公司）；Kiel University (CAU)（基尔大学（克里斯蒂安-阿尔布雷希茨大学））
Semiconductor lasers have been rapidly evolving to meet the demands of next-generation optical networks. This imposes much more stringent requirements on the laser reliability, which are dominated by degradation mechanisms (e.g., sudden degradation) limiting the semiconductor laser lifetime. Physics-based approaches are often used to characterize the degradation behavior analytically, yet explicit domain knowledge and accurate mathematical models are required. Building such models can be very challenging due to a lack of a full understanding of the complex physical processes inducing the degra…
Battery State of Health Estimation Using LLM Framework
Purdue University（普渡大学）；Drexel University（德雷塞尔大学）；Stanford University（斯坦福大学）；Indian Institute of Science（印度科学学院）；Temsa Research & Development Center（Temsa研发中心）
DriftGuard: A Hierarchical Framework for Concept Drift Detection and Remediation in Supply Chain Forecasting
Department of Computer Science Engineering, Muffakham Jah College of Engineering and Technology（穆法卡姆·贾赫工程与技术学院计算机科学工程系）；Department of CSE, Shadan Women's College of Engineering and Technology（沙达恩女子工程与技术学院计算机科学与工程系）
With the wide spread of sensors and smart devices in recent years, the data generation speed of the Internet of Things (IoT) systems has increased dramatically. In IoT systems, massive volumes of data must be processed, transformed, and analyzed on a frequent basis to enable various IoT services and functionalities. Machine Learning (ML) approaches have shown their capacity for IoT data analytics. However, applying ML models to IoT data analytics tasks still faces many difficulties and challenges, specifically, effective model selection, design/tuning, and updating, which have brought massive …
PyScrew: A Comprehensive Dataset Collection from Industrial Screw Driving Experiments
RIF Institute for Research and Transfer e.V.（RIF研究所）；Institute for Production Systems Technical University Dortmund（生产系统研究所）；Centre for Advanced Manufacturing University of Technology Sydney（先进制造中心）
TS-Haystack: A Multi-Task Retrieval Benchmark for Long-Context Time-Series Reasoning
Agentic Systems Lab, ETH Zurich（1 非常规系统实验室，苏黎世联邦理工学院）；Stanford University（2 斯坦福大学）；Traffic Engineering Group, Institute for Transport Planning and Systems, ETH Zurich（3 交通工程组，交通规划与系统研究所，苏黎世联邦理工学院）；University of Illinois Urbana-Champaign（4 印第安纳大学厄巴纳-香槟分校）；Google（5 谷歌）；Centre for Digital Health Interventions, ETH Zurich（6 数字健康干预中心，苏黎世联邦理工学院）；Centre for Digital Health Interventions, University of St. Gallen（7 数字健康干预中心，圣加尔登大学）
