Web server load prediction and anomaly detection from hypertext transfer protocol logs
نویسندگان
چکیده
<p><span lang="EN-US">As network traffic increases and new intrusions occur, anomaly detection solutions based on machine learning are necessary to detect previously unknown intrusion patterns. Most of the developed models require a labelled dataset, which can be challenging owing shortage publicly available datasets. These datasets often too small effectively train models, further motivates use real unlabeled traffic. By using traffic, it is possible more accurately simulate types anomalies that might occur in real-world improve performance model. We present method able predict categorize without aid demonstrating model’s usability while also gathering dataset from noisy The proposed long short-term memory (LTSM) system was tested setting an antivirus company successful detecting various 5-minute windowing over both predicted update curves thereby its usefulness. Our contribution development robust model generally applicable any hypertext transfer protocol (HTTP) with almost real-time detection, outperforming earlier studies terms prediction accuracy.</span></p>
منابع مشابه
A Survey of Anomaly Detection Using Data Mining Methods for Hypertext Transfer Protocol Web Services
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ژورنال
عنوان ژورنال: International Journal of Power Electronics and Drive Systems
سال: 2023
ISSN: ['2722-2578', '2722-256X']
DOI: https://doi.org/10.11591/ijece.v13i5.pp5165-5178