Mar 5, 2016 · In this paper, an semi-unsupervised anomaly detection method for HTTP traffic anomaly detection is proposed. We have evaluated our method ...
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The main contribution of this paper is the semi-unsupervised anomaly detection method for HTTP traffic anomaly detection. We made the assumption that during the ...
The main contribution of this paper is the semi-unsupervised anomaly detection method for HTTP traffic anomaly detection. We made the assumption that during the ...
Apr 28, 2023 · Semi-supervised Anomaly Detection: This approach uses a combination of labeled and unlabeled data to train the model. The model learns to ...
Mar 13, 2024 · Supervised methods are used to detect anomaly traffic [1, 2, 3, 4, 5, 6] . For example, a machine learning classification model, trained on ...
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Feb 8, 2023 · Most semi-supervised learning methods (e.g., FixMatch, VIME) assume that the labeled and unlabeled data come from the same distributions.
Missing: HTTP | Show results with:HTTP
This paper proposes the Anomaly Detection Approach based on Ensemble Semi-Supervised Active Learning (ADESSA) method, which can effectively detect traffic with ...
Missing: HTTP | Show results with:HTTP
The goal of this project is to present different machine learning methods for anomaly detection. We have constructed three different datasets that were used to ...
Missing: Traffic. | Show results with:Traffic.
Dec 19, 2023 · Powered by AI, machine learning techniques are leveraged to detect anomalous behavior through three different detection methods.
Missing: HTTP | Show results with:HTTP
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