@inproceedings{7942f588211e444fbf0936da2da85810,
title = "An Anomaly Detection Framework for System Logs Based on Ensemble Learning",
abstract = "Logs offer vital insights into system states and contextual details, crucial for identifying anomalies. Numerous machine learning and deep learning approaches have been proposed for log anomaly detection. Recent studies reveal that distinct software systems tend to generate a substantial volume of complexity and diversity of logs that exhibit considerable discrepancies in class distribution. In this paper, we introduce IELog, a framework for anomaly detection. IELog employs DSS (Denoise Selection Sampling) to oversample the minority class, mitigating imbalanced data impact. Subsequently, IELog proposes the AW (Anomaly Weighting) ensemble rule to effectively combine the prediction outcomes of individual base models, leveraging their distinct strengths. Extensive experiments have been performed on four different public log datasets, which demonstrate the validity of the proposed framework IELog.",
keywords = "Ensemble learning, Imbalanced data, Log anomaly detection",
author = "Wenjing Xiong and Wu Chen and Jiamou Liu and Kaiqi Zhao",
note = "Publisher Copyright: {\textcopyright} 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023 ; Conference date: 15-11-2023 Through 19-11-2023",
year = "2024",
doi = "10.1007/978-981-99-7019-3\_6",
language = "英语",
isbn = "9789819970186",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "52--65",
editor = "Fenrong Liu and Sadanandan, \{Arun Anand\} and Pham, \{Duc Nghia\} and Petrus Mursanto and Dickson Lukose",
booktitle = "PRICAI 2023",
address = "德国",
}