On the nature and types of anomalies: a review of deviations in data
R Foorthuis - International journal of data science and analytics, 2021 - Springer
Anomalies are occurrences in a dataset that are in some way unusual and do not fit the
general patterns. The concept of the anomaly is typically ill defined and perceived as vague …
general patterns. The concept of the anomaly is typically ill defined and perceived as vague …
A survey of public IoT datasets for network security research
F De Keersmaeker, Y Cao… - … Surveys & Tutorials, 2023 - ieeexplore.ieee.org
Publicly available datasets are an indispensable tool for researchers, as they allow testing
new algorithms on a wide range of different scenarios and making scientific experiments …
new algorithms on a wide range of different scenarios and making scientific experiments …
TON_IoT telemetry dataset: A new generation dataset of IoT and IIoT for data-driven intrusion detection systems
Although the Internet of Things (IoT) can increase efficiency and productivity through
intelligent and remote management, it also increases the risk of cyber-attacks. The potential …
intelligent and remote management, it also increases the risk of cyber-attacks. The potential …
Sensor-fault detection, isolation and accommodation for digital twins via modular data-driven architecture
H Darvishi, D Ciuonzo, ER Eide… - IEEE Sensors …, 2020 - ieeexplore.ieee.org
Sensor technologies empower Industry 4.0 by enabling integration of in-field and real-time
raw data into digital twins. However, sensors might be unreliable due to inherent issues …
raw data into digital twins. However, sensors might be unreliable due to inherent issues …
Fault diagnosis based on extremely randomized trees in wireless sensor networks
Abstract Wireless Sensor Network (WSN) being highly diversified cyber–physical system
makes it vulnerable to numerous failures, which can cause devastation towards safety …
makes it vulnerable to numerous failures, which can cause devastation towards safety …
A survey of clustering algorithms for big data: Taxonomy and empirical analysis
Clustering algorithms have emerged as an alternative powerful meta-learning tool to
accurately analyze the massive volume of data generated by modern applications. In …
accurately analyze the massive volume of data generated by modern applications. In …
A distributed sensor-fault detection and diagnosis framework using machine learning
SU Jan, YD Lee, IS Koo - Information Sciences, 2021 - Elsevier
The objective of this work is to design a sensor-fault detection and diagnosis system for the
Internet of Things and Cyber-Physical Systems. The challenge is, however, achieving this …
Internet of Things and Cyber-Physical Systems. The challenge is, however, achieving this …
Fault detection in wireless sensor networks through SVM classifier
Wireless sensor networks (WSNs) are prone to many failures such as hardware failures,
software failures, and communication failures. The fault detection in WSNs is a challenging …
software failures, and communication failures. The fault detection in WSNs is a challenging …
A machine-learning architecture for sensor fault detection, isolation, and accommodation in digital twins
Sensor technologies empower Industry 4.0 by enabling integration of in-field and real-time
raw data into digital twins (DTs). However, sensors might be unreliable due to inherent …
raw data into digital twins (DTs). However, sensors might be unreliable due to inherent …
Datasets are not enough: Challenges in labeling network traffic
In contrast to previous surveys, the present work is not focused on reviewing the datasets
used in the network security field. The fact is that many of the available public labeled …
used in the network security field. The fact is that many of the available public labeled …