Improvement of the Log Pattern Extracting Algorithm Using Text Similarity

Zhao, Yining; Xiao-dong, Wang; Xiao, Haili; Chi, Xuebin

doi:10.1109/ipdpsw.2018.00087

Cited by 10 publications

(3 citation statements)

References 20 publications

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“…Log files are analyzed for different purposes, such as troubleshooting [16], usage statistics [17], security monitoring [18] and performance profiling [19]. Quite a few methodologies have been applied to deduce what the corresponding person is interested in, by analyzing user log file records [20].…”

Section: Related Workmentioning

confidence: 99%

User Behavior Analysis by Cross-Domain Log Data Fusion

et al. 2020

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Modern mass customization production allows user interaction activities to be distributed in the full product life cycle via multiple information systems. Investigating user behaviors across the boundaries of different domains helps to deeply integrate isolated fragmental profiles into a comprehensive one, and therefore can provide multi-dimensional, high-quality and valuable services. However, traditional user behavior analysis models are based on individual user profile information derived from separate domains, such as requirement analysis, design, supply chain, logistics, marketing, etc., which have not considered the whole complexity of mass customization manufacturing. In this paper, we introduce the concept of multidimensional semantic activity space, where user behavior features are merged and represented as combined vectors. User behavior patterns are discovered by mining action data extracted from log files in different subsystems in the corresponding domains. We also identify distinct categories of user behaviors in various modules and subsystems in the context of an intelligent manufacturing environment. Experiment results show a strong indication that the proposed approach can be applied to reveal variations in typical behavioral aspects of cross-domain participants, in terms of patterns in resource access, operation tasks, performance assessment, etc.

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Section: Related Workmentioning

confidence: 99%

User Behavior Analysis by Cross-Domain Log Data Fusion

et al. 2020

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“…extraction algorithm [22] to generate log types. For SCE traces which only contains variables, we construct a data cube that stores information of all traces and can produce log types for any variable types.…”

Section: Pos(isgc2019)033mentioning

confidence: 99%

“…First, we pick up system logs and middleware logs from CNGrid as the target to be analyzed, according to our demands of responding to system failures, defending on malicious attacks and providing user services. Second, log pattern extraction algorithms [22] are used to classify types for system logs and middleware logs. We organize these log types as the log library and implement interfaces to access it.…”

mentioning

confidence: 99%

A Blueprint of Log Based Monitoring and Diagnosing Framework in Large Distributed Environments

Zhao¹,

Wang²,

Xiao³

et al. 2019

Proceedings of International Symposium on Grids &Amp; Clouds 2019 — PoS(ISGC2019)

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Distributed systems have kept scaling upward since this concept appears, and they soon evolve to environments that contain heterogeneous components playing different roles, making it difficult to understand how the large environment works or if any undesired matters happened from security point of view. Logs, produced by devices, subsystems and running processes, are a very important source to help system maintainers to get relative security knowledge. But there are too many logs and too many kinds of logs to deal with, which makes manual checking impossible. In this work we will share some of our experiences in log processing and analyzing. We have summarized some common major steps that appear in most of the existing log analysis approaches, including log selection, log classification, information analyses and result feedback. We also represent a general framework that monitors events, analyzes hidden information and diagnoses the healthy state for large distributed computing environments bases on logs. Although the framework we initially designed was for the maintenance for CNGrid, its process is adaptable to other distributed computing environments.

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A GMM-Based Anomaly IP Detection Model from Security Logs

Zhou

2021

Lecture Notes in Computer Science

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Improvement of the Log Pattern Extracting Algorithm Using Text Similarity

Cited by 10 publications

References 20 publications

User Behavior Analysis by Cross-Domain Log Data Fusion

User Behavior Analysis by Cross-Domain Log Data Fusion

A Blueprint of Log Based Monitoring and Diagnosing Framework in Large Distributed Environments

A GMM-Based Anomaly IP Detection Model from Security Logs

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