scholar.google.com › citations
Aug 10, 2022 · This paper proposes a supervised learning method with manual participation. We introduce the integrated learning model and train a supervised anomaly detection ...
People also ask
What is ensemble model for anomaly detection?
Which type of machine learning is best for anomaly detection?
What technology is being used to detect anomalies?
What are the three basic approaches to anomaly detection?
This work proposed and a novel anomaly detection approach based on ensemble semi-supervised active learning, which can effectively detect anomalous traffic.
In this paper, we introduce a domain-agonistic, end-to-end, ensemble-based deep active learning framework for anomaly detection. By leveraging unsupervised and ...
This paper conducts experiments on abnormal traffic datasets in the software-defined network environment, calculates precision, recall and F1-score,
Most of the popular unsupervised anomaly detection algorithms have been adapted to the active learning setting. Some of these methods are based on ensembles and ...
Sep 30, 2024 · We propose Mateen, an online learning framework designed to augment the capabilities of offline DAEs, enabling them to recognize and adapt to changing benign ...
Sep 28, 2022 · This paper conducts experiments on abnormal traffic datasets in the software-defined network environment, calculates precision, recall and F1- ...
The results show that in addition to discovering significantly more anomalies than state-of-the-art unsupervised baselines, the active learning algorithms ...
Missing: Technology | Show results with:Technology
Feb 22, 2022 · We propose a novel approach, called WisCon (Wisdom of the Contexts), to effectively detect complex contextual anomalies in situations where the true contextual ...
Jun 12, 2024 · In this paper, we present a comprehensive study on using ensemble machine learning methods for enhancing IoT cybersecurity via anomaly detection.
Missing: Active | Show results with:Active