A review of local outlier factor algorithms for outlier detection in big data streams

O Alghushairy, R Alsini, T Soule, X Ma - Big Data and Cognitive …, 2020 - mdpi.com
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 …

A comprehensive survey on machine learning for networking: evolution, applications and research opportunities

R Boutaba, MA Salahuddin, N Limam, S Ayoubi… - Journal of Internet …, 2018 - Springer
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 …

DÏoT: A federated self-learning anomaly detection system for IoT

TD Nguyen, S Marchal, M Miettinen… - 2019 IEEE 39th …, 2019 - ieeexplore.ieee.org
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 …

Data fusion approach for collaborative anomaly intrusion detection in blockchain-based systems

W Liang, L **ao, K Zhang, M Tang… - IEEE Internet of Things …, 2021 - ieeexplore.ieee.org
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 …

Community detection in networks: A multidisciplinary review

MA Javed, MS Younis, S Latif, J Qadir, A Baig - Journal of Network and …, 2018 - Elsevier
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 …

A survey of network anomaly detection techniques

M Ahmed, AN Mahmood, J Hu - Journal of Network and Computer …, 2016 - Elsevier
Abstract Information and Communication Technology (ICT) has a great impact on social
wellbeing, economic growth and national security in todays world. Generally, ICT includes …

A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data

M Goldstein, S Uchida - PloS one, 2016 - journals.plos.org
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 …

[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 …

Network anomaly detection: methods, systems and tools

MH Bhuyan, DK Bhattacharyya… - … surveys & tutorials, 2013 - ieeexplore.ieee.org
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 …

Outlier detection for temporal data: A survey

M Gupta, J Gao, CC Aggarwal… - IEEE Transactions on …, 2013 - ieeexplore.ieee.org
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 …