Machine learning for anomaly detection: A systematic review
Anomaly detection has been used for decades to identify and extract anomalous
components from data. Many techniques have been used to detect anomalies. One of the …
components from data. Many techniques have been used to detect anomalies. One of the …
Machine learning in natural and engineered water systems
R Huang, C Ma, J Ma, X Huangfu, Q He - Water Research, 2021 - Elsevier
Water resources of desired quality and quantity are the foundation for human survival and
sustainable development. To better protect the water environment and conserve water …
sustainable development. To better protect the water environment and conserve water …
Forecasting and Anomaly Detection approaches using LSTM and LSTM Autoencoder techniques with the applications in supply chain management
Making appropriate decisions is indeed a key factor to help companies facing challenges
from supply chains nowadays. In this paper, we propose two data-driven approaches that …
from supply chains nowadays. In this paper, we propose two data-driven approaches that …
Attack classification of an intrusion detection system using deep learning and hyperparameter optimization
A network intrusion detection system (NIDS) is a solution that mitigates the threat of attacks
on a network. The success of a NIDS depends on the success of its algorithm and the …
on a network. The success of a NIDS depends on the success of its algorithm and the …
A review of machine learning and deep learning techniques for anomaly detection in IoT data
R Al-amri, RK Murugesan, M Man, AF Abdulateef… - Applied Sciences, 2021 - mdpi.com
Anomaly detection has gained considerable attention in the past couple of years. Emerging
technologies, such as the Internet of Things (IoT), are known to be among the most critical …
technologies, such as the Internet of Things (IoT), are known to be among the most critical …
A novel approach for network intrusion detection using multistage deep learning image recognition
The current rise in hacking and computer network attacks throughout the world has
heightened the demand for improved intrusion detection and prevention solutions. The …
heightened the demand for improved intrusion detection and prevention solutions. The …
[PDF][PDF] FlowLens: Enabling Efficient Flow Classification for ML-based Network Security Applications.
An emerging trend in network security consists in the adoption of programmable switches for
performing various security tasks in large-scale, high-speed networks. However, since …
performing various security tasks in large-scale, high-speed networks. However, since …
Robust unsupervised anomaly detection via multi-time scale DCGANs with forgetting mechanism for industrial multivariate time series
H Liang, L Song, J Wang, L Guo, X Li, J Liang - Neurocomputing, 2021 - Elsevier
Detecting anomalies in time series is a vital technique in a wide variety of industrial
application in which sensors monitor expensive machinery. The complexity of this task …
application in which sensors monitor expensive machinery. The complexity of this task …
Machine learning for 5G security: Architecture, recent advances, and challenges
The granularization of crucial network functions implementation using software-centric, and
virtualized approaches in 5G networks have brought forth unprecedented security …
virtualized approaches in 5G networks have brought forth unprecedented security …
Fuzzy c-means-based isolation forest
The problem of finding anomalies (outliers) in databases is one of the most important issues
in modern data analysis. One of the reasons is the occurrence of this issue in almost every …
in modern data analysis. One of the reasons is the occurrence of this issue in almost every …