SemLog: A Semantics-based Approach for Anomaly Detection in Big Data System Logs-Reference-Cited by-同舟云学术

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SemLog: A Semantics-based Approach for Anomaly Detection in Big Data System Logs

Published:2023-12-17 Issue: Volume: Page:1199-1206
ISSN:
Container-title:2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS)
language:
Short-container-title:

Author:

Tan Xiaoman¹,Han Ningning²,Lu Siyang²,Chen Wu¹,Wang Dongdong³

Affiliation:

1. College of Computer and Information Science, College of Software, Southwest University,Chongqing,China

2. School of Computer and Information Technology, Beijing Jiaotong University,Beijing,China

3. Environmental and Construction Engineering, University of Central Florida,Department of Civil,Orlando,FL,US

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10475889/10475881/10475923.pdf?arnumber=10475923

Reference36 articles.

1. Efficient Online Scheduling for Coflow-aware Machine Learning Clusters

2. DarkTE: Towards Dark Traffic Engineering in Data Center Networks with Ensemble Learning

3. BULB: Lightweight and Automated Load Balancing for Fast Datacenter Networks

4. Robust log-based anomaly detection on unstable log data

5. HitAnomaly: Hierarchical Transformers for Anomaly Detection in System Log

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