During the past decade, many anomaly detection approaches have been introduced in different fields such as network monitoring, fraud detection, and intrusion detection. However, they require understanding of data pattern and often need a long off-line period to build a model or network for the target data. Providing real-time and proactive anomaly detection for streaming time series without human intervention and domain knowledge is highly valuable since it greatly reduces human effort and enables appropriate countermeasures to be undertaken before a disastrous damage, failure, or other harmful event occurs. However, this issue has not been well studied yet. To address it, this paper proposes RePAD, which is a Real-time Proactive Anomaly Detection algorithm for streaming time series based on Long Short-Term Memory (LSTM). RePAD utilizes short-term historic data points to predict and determine whether or not the upcoming data point is a sign that an anomaly is likely to happen in the near future. By dynamically adjusting the detection threshold over time, RePAD is able to tolerate minor pattern change in time series and detect anomalies either proactively or on time. Experiments based on two time series datasets collected from the Numenta Anomaly Benchmark demonstrate that RePAD is able to proactively detect anomalies and provide early warnings in real time without human intervention and domain knowledge.
Høgskulen på Vestlandet (HVL)（西挪威应用科学大学）；Norwegian University of Science and Technology（挪威科技大学）
An open-ended time series refers to a series of data points indexed in time order without an end. Such a time series can be found everywhere due to the prevalence of Internet of Things. Providing lightweight and real-time anomaly detection for open-ended time series is highly desirable to industry and organizations since it allows immediate response and avoids potential financial loss. In the last few years, several real-time time series anomaly detection approaches have been introduced. However, they might exhaust system resources when they are applied to open-ended time series for a long tim…
Norwegian University of Science and Technology（挪威科技大学）；Simula Research Laboratory（西穆拉研究实验室）
Anomaly detection is the process of identifying unexpected events or ab-normalities in data, and it has been applied in many different areas such as system monitoring, fraud detection, healthcare, intrusion detection, etc. Providing real-time, lightweight, and proactive anomaly detection for time series with neither human intervention nor domain knowledge could be highly valuable since it reduces human effort and enables appropriate countermeasures to be undertaken before a disastrous event occurs. To our knowledge, RePAD (Real-time Proactive Anomaly Detection algorithm) is a generic approach …
Sentinel: Scheduling Live Streams with Proactive Anomaly Detection in Crowdsourced Cloud-Edge Platforms
College of Intelligence and Computing, Tianjin University（天津大学智能与计算学院）；Faculty of Digital Economics and Managements, Tianjin University of Finance and Economics（天津财经大学数字经济与管理学院）；College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机科学与软件工程学院）
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Despite the rapid advancement of navigation algorithms, mobile robots often produce anomalous behaviors that can lead to navigation failures. The ability to detect such anomalous behaviors is a key component in modern robots to achieve high-levels of autonomy. Reactive anomaly detection methods identify anomalous task executions based on the current robot state and thus lack the ability to alert the robot before an actual failure occurs. Such an alert delay is undesirable due to the potential damage to both the robot and the surrounding objects. We propose a proactive anomaly detection network…
JR East Information Systems（东日本旅客铁道信息系统）；PARC, A Xerox Company（施乐帕克研究中心）
Although training data is essential for machine learning, railway companies are facing difficulties in gathering adequate images of defective equipment due to their proactive replacement of would be defective equipment. Nevertheless, proactive replacement is indispensable for safe and undisturbed operation of public transport. In this research, we have developed a model using CycleGAN to generate artificial images of defective equipment instead of real images. By adopting these generated images as training data, we verified that these images are indistinguishable from real images and they play…
Norwegian University of Science and Technology（挪威科技大学）；Simula Research Laboratory（西穆拉研究实验室）
Anomaly detection is an active research topic in many different fields such as intrusion detection, network monitoring, system health monitoring, IoT healthcare, etc. However, many existing anomaly detection approaches require either human intervention or domain knowledge, and may suffer from high computation complexity, consequently hindering their applicability in real-world scenarios. Therefore, a lightweight and ready-to-go approach that is able to detect anomalies in real-time is highly sought-after. Such an approach could be easily and immediately applied to perform time series anomaly d…
HyPV-LEAD: Proactive Early-Warning of Cryptocurrency Anomalies through Data-Driven Structural-Temporal Modeling
Department of Business Administration（商业管理系）；Kumoh National Institute of Technology（Kumoh国立技术学院）；Ewha Womans University（成均馆大学）；Ewha Womans University, Coretrustlink（成均馆大学，Coretrustlink）
Protecting the networks of tomorrow is set to be a challenging domain due to increasing cyber security threats and widening attack surfaces created by the Internet of Things (IoT), increased network heterogeneity, increased use of virtualisation technologies and distributed architectures. This paper proposes SDS (Software Defined Security) as a means to provide an automated, flexible and scalable network defence system. SDS will harness current advances in machine learning to design a CNN (Convolutional Neural Network) using NAS (Neural Architecture Search) to detect anomalous network traffic.…
We present a decision support system for managing water quality in prawn ponds. The system uses various sources of data and deep learning models in a novel way to provide 24-hour forecasting and anomaly detection of water quality parameters. It provides prawn farmers with tools to proactively avoid a poor growing environment, thereby optimising growth and reducing the risk of losing stock. This is a major shift for farmers who are forced to manage ponds by reactively correcting poor water quality conditions. To our knowledge, we are the first to apply Transformer as an anomaly detection model,…
LG Electronics（LG电子）；Advanced AI, America Research Lab（美国研究院先进人工智能实验室）
In recent times, advances in artificial intelligence (AI) and IoT have enabled seamless and viable maintenance of appliances in home and building environments. Several studies have shown that AI has the potential to provide personalized customer support which could predict and avoid errors more reliably than ever before. In this paper, we have analyzed the various building blocks needed to enable a successful AI-driven predictive maintenance use-case. Unlike, existing surveys which mostly provide a deep dive into the recent AI algorithms for Predictive Maintenance (PdM), our survey provides th…
Organizations rely heavily on time series metrics to measure and model key aspects of operational and business performance. The ability to reliably detect issues with these metrics is imperative to identifying early indicators of major problems before they become pervasive. It can be very challenging to proactively monitor a large number of diverse and constantly changing time series for anomalies, so there are often gaps in monitoring coverage, disabled or ignored monitors due to false positive alarms, and teams resorting to manual inspection of charts to catch problems. Traditionally, variat…
EAGLE: Edge-Aware Graph Learning for Proactive Delivery Delay Prediction in Smart Logistics Networks
College of Engineering, Northeastern University（东北大学工程学院）；School of Engineering, Santa Clara University（圣克拉拉大学工程学院）；Department of Chemistry and Chemical Biology, University of New Mexico（新墨西哥大学化学与化学生物学系）
Credit Card Fraud Detection in the Nigerian Financial Sector: A Comparison of Unsupervised TensorFlow-Based Anomaly Detection Techniques, Autoencoders and PCA Algorithm
Enugu State University of Science and Technology（埃努古州立科技大学）
Forecasting Anomaly Precursors via Uncertainty-Aware Time-Series Ensembles
Department of Industrial & Management Engineering, Korea University（韩国大学工业与管理工程系）；Department of Industrial Engineering, Seoul National University（首尔国立大学工业工程系）；LG CNS
Research on Dynamic Data Flow Anomaly Detection based on Machine Learning
Olin Business School（奥林商学院）；Columbia University（哥伦比亚大学）；The Fu Foundation School of Engineering and Applied Science（傅氏基金会工程与应用科学学院）；Tulane University（杜兰大学）；A. B. Freeman School of Business（A.B.弗里曼商学院）；Northeastern University（东北大学）；College of Computer Sciences（计算机科学学院）；Courant Institute of Mathematical Sciences（柯朗数学科学研究所）
As developing countries continue to face challenges associated with infectious diseases, the need to improve infrastructure to systematically collect data which can be used to understand their outbreak patterns becomes more critical. The World Health Organization (WHO) Integrated Disease Surveillance and Response (IDSR) strategy seeks to drive the systematic collection of surveillance data to strengthen district-level reporting and to translate them into public health actions. Since the analysis of this surveillance data at the central levels of government in many developing nations has tradit…
Proactive network maintenance (PNM) is the concept of using data from a network to identify and locate network faults, many or all of which could worsen to become service failures. The separation between the network fault and the service failure affords early detection of problems in the network to allow PNM to take place. Consequently, PNM is a form of prognostics and health management (PHM). The problem of localizing and classifying anomalies on 1-dimensional data series has been under research for years. We introduce a new algorithm that leverages Deep Convolutional Neural Networks to effic…
SILS: Strategic Influence on Liquidity Stability and Whale Detection in Concentrated-Liquidity DEXs
Department of Computer Engineering SR.C, Islamic Azad University（计算机工程系（SR.C），伊斯兰教Azad大学）；Department of Computer Engineering Iran university of Science and Technology（计算机工程系，伊朗科学技术大学）
Spatio-Temporal Grid Intelligence: A Hybrid Graph Neural Network and LSTM Framework for Robust Electricity Theft Detection
Department Computer Science, Abiola Ajimobi Technical University（计算机科学系，阿比奥米博技术大学）；Department of Chemical and Petroleum Engineering, Abiola Ajimobi Technical University（化学与石油工程系，阿比奥米博技术大学）
