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Anomaly detection for IoT time-series data: A survey
Anomaly detection is a problem with applications for a wide variety of domains; it involves
the identification of novel or unexpected observations or sequences within the data being …
the identification of novel or unexpected observations or sequences within the data being …
A systematic literature review of IoT time series anomaly detection solutions
The rapid spread of the Internet of Things (IoT) devices has prompted many people and
companies to adopt the IoT paradigm, as this paradigm allows the automation of several …
companies to adopt the IoT paradigm, as this paradigm allows the automation of several …
Deep learning for anomaly detection: A survey
Anomaly detection is an important problem that has been well-studied within diverse
research areas and application domains. The aim of this survey is two-fold, firstly we present …
research areas and application domains. The aim of this survey is two-fold, firstly we present …
A survey of AI-based anomaly detection in IoT and sensor networks
Machine learning (ML) and deep learning (DL), in particular, are common tools for anomaly
detection (AD). With the rapid increase in the number of Internet-connected devices, the …
detection (AD). With the rapid increase in the number of Internet-connected devices, the …
Sequential (quickest) change detection: Classical results and new directions
Online detection of changes in stochastic systems, referred to as sequential change
detection or quickest change detection, is an important research topic in statistics, signal …
detection or quickest change detection, is an important research topic in statistics, signal …
[HTML][HTML] A comparative study on online machine learning techniques for network traffic streams analysis
Modern networks generate a massive amount of traffic data streams. Analyzing this data is
essential for various purposes, such as network resources management and cyber-security …
essential for various purposes, such as network resources management and cyber-security …
Drift-aware methodology for anomaly detection in smart grid
Energy efficiency and sustainability are important factors to address in the context of smart
cities. In this sense, smart metering and nonintrusive load monitoring play a crucial role in …
cities. In this sense, smart metering and nonintrusive load monitoring play a crucial role in …
When model meets new normals: test-time adaptation for unsupervised time-series anomaly detection
Time-series anomaly detection deals with the problem of detecting anomalous timesteps by
learning normality from the sequence of observations. However, the concept of normality …
learning normality from the sequence of observations. However, the concept of normality …
Deep learning for encrypted traffic classification in the face of data drift: An empirical study
Deep learning models have shown to achieve high performance in encrypted traffic
classification. However, when it comes to production use, multiple factors challenge the …
classification. However, when it comes to production use, multiple factors challenge the …
Mobile trajectory anomaly detection: Taxonomy, methodology, challenges, and directions
The growing number of cars on city roads has led to an increase in traffic accidents,
highlighting the need for traffic safety measures. Mobile trajectory anomaly detection is an …
highlighting the need for traffic safety measures. Mobile trajectory anomaly detection is an …