Real-time big data processing for anomaly detection: A survey
The advent of connected devices and omnipresence of Internet have paved way for
intruders to attack networks, which leads to cyber-attack, financial loss, information theft in …
intruders to attack networks, which leads to cyber-attack, financial loss, information theft in …
Distributed data stream processing and edge computing: A survey on resource elasticity and future directions
Under several emerging application scenarios, such as in smart cities, operational
monitoring of large infrastructure, wearable assistance, and Internet of Things, continuous …
monitoring of large infrastructure, wearable assistance, and Internet of Things, continuous …
Spatiotemporal data mining: a survey on challenges and open problems
Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay
between space and time. Several available surveys capture STDM advances and report a …
between space and time. Several available surveys capture STDM advances and report a …
Combating the challenges of false positives in AI-driven anomaly detection systems and enhancing data security in the cloud
Anomaly detection is critical for network security, fraud detection, and system health
monitoring applications. Traditional methods like statistical approaches and distance-based …
monitoring applications. Traditional methods like statistical approaches and distance-based …
[HTML][HTML] Anomaly detection for space information networks: A survey of challenges, techniques, and future directions
Abstract Space anomaly detection plays a critical role in safeguarding the integrity and
reliability of space systems amid the rising tide of threats. This survey aims to deepen …
reliability of space systems amid the rising tide of threats. This survey aims to deepen …
Learning to classify with incremental new class
New class detection and effective model expansion are of great importance in incremental
data mining. In open incremental data environments, data often come with novel classes, eg …
data mining. In open incremental data environments, data often come with novel classes, eg …
Online anomaly detection with concept drift adaptation using recurrent neural networks
Anomaly detection in time series is an important task with several practical applications. The
common approach of training one model in an offline manner using historical data is likely to …
common approach of training one model in an offline manner using historical data is likely to …
Detecting outliers in a univariate time series dataset using unsupervised combined statistical methods: A case study on surface water temperature
The surface water temperature is a vital ecological and climate variable, and its monitoring is
critical. An extensive sensor network measures the ocean, but outliers pervade the …
critical. An extensive sensor network measures the ocean, but outliers pervade the …
Clustering‐based real‐time anomaly detection—A breakthrough in big data technologies
Off late, the ever increasing usage of a connected Internet‐of‐Things devices has
consequently augmented the volume of real‐time network data with high velocity. At the …
consequently augmented the volume of real‐time network data with high velocity. At the …
Machine learning-based anomaly detection of groundwater microdynamics: case study of Chengdu, China
H Shi, J Guo, Y Deng, Z Qin - Scientific Reports, 2023 - nature.com
Detection of subsurface hydrodynamic anomalies plays a significant role in groundwater
resource management and environmental monitoring. In this paper, based on data from the …
resource management and environmental monitoring. In this paper, based on data from the …