A review of local outlier factor algorithms for outlier detection in big data streams
Outlier detection is a statistical procedure that aims to find suspicious events or items that
are different from the normal form of a dataset. It has drawn considerable interest in the field …
are different from the normal form of a dataset. It has drawn considerable interest in the field …
A comprehensive survey on machine learning for networking: evolution, applications and research opportunities
Abstract Machine Learning (ML) has been enjoying an unprecedented surge in applications
that solve problems and enable automation in diverse domains. Primarily, this is due to the …
that solve problems and enable automation in diverse domains. Primarily, this is due to the …
DÏoT: A federated self-learning anomaly detection system for IoT
IoT devices are increasingly deployed in daily life. Many of these devices are, however,
vulnerable due to insecure design, implementation, and configuration. As a result, many …
vulnerable due to insecure design, implementation, and configuration. As a result, many …
Data fusion approach for collaborative anomaly intrusion detection in blockchain-based systems
Blockchain technology is rapidly changing the transaction behavior and efficiency of
businesses in recent years. Data privacy and system reliability are critical issues that is …
businesses in recent years. Data privacy and system reliability are critical issues that is …
Community detection in networks: A multidisciplinary review
The modern science of networks has made significant advancement in the modeling of
complex real-world systems. One of the most important features in these networks is the …
complex real-world systems. One of the most important features in these networks is the …
A survey of network anomaly detection techniques
Abstract Information and Communication Technology (ICT) has a great impact on social
wellbeing, economic growth and national security in todays world. Generally, ICT includes …
wellbeing, economic growth and national security in todays world. Generally, ICT includes …
A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data
Anomaly detection is the process of identifying unexpected items or events in datasets,
which differ from the norm. In contrast to standard classification tasks, anomaly detection is …
which differ from the norm. In contrast to standard classification tasks, anomaly detection is …
[LIBRO][B] An introduction to outlier analysis
CC Aggarwal, CC Aggarwal - 2017 - Springer
Outliers are also referred to as abnormalities, discordants, deviants, or anomalies in the data
mining and statistics literature. In most applications, the data is created by one or more …
mining and statistics literature. In most applications, the data is created by one or more …
Network anomaly detection: methods, systems and tools
Network anomaly detection is an important and dynamic research area. Many network
intrusion detection methods and systems (NIDS) have been proposed in the literature. In this …
intrusion detection methods and systems (NIDS) have been proposed in the literature. In this …
Outlier detection for temporal data: A survey
In the statistics community, outlier detection for time series data has been studied for
decades. Recently, with advances in hardware and software technology, there has been a …
decades. Recently, with advances in hardware and software technology, there has been a …