Isolation forest based anomaly detection: A systematic literature review
WS Al Farizi, I Hidayah, MN Rizal - 2021 8th International …, 2021 - ieeexplore.ieee.org
Anomaly detection using machine learning algorithms is rising lately, especially with
increased data volume and velocity. One of the most recent anomaly detection algorithms is …
increased data volume and velocity. One of the most recent anomaly detection algorithms is …
Multiscale network traffic prediction method based on deep echo-state network for internet of things
As a typical Internet of Things application, network traffic prediction (NTP) plays a decisive
role in congestion control, resource allocation, and anomaly detection. The trend of network …
role in congestion control, resource allocation, and anomaly detection. The trend of network …
[HTML][HTML] UInDeSI4. 0: An efficient Unsupervised Intrusion Detection System for network traffic flow in Industry 4.0 ecosystem
Abstract In an Industry 4.0 ecosystem, all the essential components are digitally
interconnected, and automation is integrated for higher productivity. However, it invites the …
interconnected, and automation is integrated for higher productivity. However, it invites the …
Mining workflows for anomalous data transfers
Modern scientific workflows are data-driven and are often executed on distributed,
heterogeneous, high-performance computing infrastructures. Anomalies and failures in the …
heterogeneous, high-performance computing infrastructures. Anomalies and failures in the …
Deep-fda: Using functional data analysis and neural networks to characterize network services time series
D Perdices, JEL de Vergara… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
In network management, it is important to model baselines, trends, and regular behaviors to
adequately deliver network services. However, their characterization is complex, so network …
adequately deliver network services. However, their characterization is complex, so network …
Large Language Models for Anomaly Detection in Computational Workflows: From Supervised Fine-Tuning to In-Context Learning
Anomaly detection in computational workflows is critical for ensuring system reliability and
security. However, traditional rule-based methods struggle to detect novel anomalies. This …
security. However, traditional rule-based methods struggle to detect novel anomalies. This …
Self-supervised Learning for Anomaly Detection in Computational Workflows
Anomaly detection is the task of identifying abnormal behavior of a system. Anomaly
detection in computational workflows is of special interest because of its wide implications in …
detection in computational workflows is of special interest because of its wide implications in …
Monitoring of a platinum group metal flotation plant with an isolation forest
Froth flotation is one of the most important techniques in mineral processing to beneficiate
valuable minerals from ore. As a consequence, advanced control of industrial flotation plants …
valuable minerals from ore. As a consequence, advanced control of industrial flotation plants …
Unsupervised abnormal traffic detection through topological flow analysis
Cyberthreats are a permanent concern in our modern technological world. In the recent
years, sophisticated traffic analysis techniques and anomaly detection (AD) algorithms have …
years, sophisticated traffic analysis techniques and anomaly detection (AD) algorithms have …
Learning transfers via transfer learning
M Arifuzzaman, E Arslan - … on Innovating the Network for Data …, 2021 - ieeexplore.ieee.org
Detecting performance anomalies is key to efficiently utilize network resources and improve
the quality of service. Researchers proposed various approaches to identify the presence of …
the quality of service. Researchers proposed various approaches to identify the presence of …