Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.12188/23156
DC Field | Value | Language |
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dc.contributor.author | Bogatinovski, Jasmin | en_US |
dc.contributor.author | Madjarov, Gjorgji | en_US |
dc.contributor.author | Nedelkoski, Sasho | en_US |
dc.contributor.author | Cardoso, Jorge | en_US |
dc.contributor.author | Kao, Odej | en_US |
dc.date.accessioned | 2022-09-28T09:33:05Z | - |
dc.date.available | 2022-09-28T09:33:05Z | - |
dc.date.issued | 2022-07-10 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.12188/23156 | - |
dc.description.abstract | Artificial Intelligence for IT Operations (AIOps) describes the process of maintaining and operating large IT systems using diverse AI-enabled methods and tools for, e.g., anomaly detection and root cause analysis, to support the remediation, optimization, and automatic initiation of self-stabilizing IT activities. The core step of any AIOps workflow is anomaly detection, typically performed on high-volume heterogeneous data such as log messages (logs), metrics (e.g., CPU utilization), and distributed traces. In this paper, we propose a method for reliable and practical anomaly detection from system logs. It overcomes the common disadvantage of related works, i.e., the need for a large amount of manually labeled training data, by building an anomaly detection model with log instructions from the source code of 1000+ GitHub projects. The instructions from diverse systems contain rich and heterogenous information about many different normal and abnormal IT events and serve as a foundation for anomaly detection. The proposed method, named ADLILog, combines the log instructions and the data from the system of interest (target system) to learn a deep neural network model through a two-phase learning procedure. The experimental results show that ADLILog outperforms the related approaches by up to 60% on the F1 score while satisfying core non-functional requirements for industrial deployments such as unsupervised design, efficient model updates, and small model sizes. | en_US |
dc.publisher | IEEE | en_US |
dc.subject | anomaly detection, log data, system dependability, AIOps, deep learning | en_US |
dc.title | Leveraging Log Instructions in Log-based Anomaly Detection | en_US |
dc.type | Proceedings | en_US |
dc.relation.conference | 2022 IEEE International Conference on Services Computing (SCC) | en_US |
item.grantfulltext | open | - |
item.fulltext | With Fulltext | - |
Appears in Collections: | Faculty of Computer Science and Engineering: Conference papers |
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File | Description | Size | Format | |
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2207.03206.pdf | 452.94 kB | Adobe PDF | View/Open |
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