Journal article

P-Gram: Positional N-Gram for the Clustering of Machine-Generated Messages

Jiaojiao Jiang, Steve Versteeg, Jun Han, Md Arafat Hossain, Jean-Guy Schneider, Christopher Leckie, Zeinab Farahmandpour

IEEE Access | Institute of Electrical and Electronics Engineers (IEEE) | Published : 2019

Abstract

An IT system generates messages for other systems or users to consume, through direct interaction or as system logs. Automatically identifying the types of these machine-generated messages has many applications, such as intrusion detection and system behavior discovery. Among various heuristic methods for automatically identifying message types, the clustering methods based on keyword extraction have been quite effective. However, these methods still suffer from keyword misidentification problems, i.e., some keyword occurrences are wrongly identified as payload and some strings in the payload are wrongly identified as keyword occurrences, leading to the misidentification of the message types..

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Grants

Awarded by Australian Research Council Linkage Project "Generating Virtual Deployment Environments for Enterprise Software Systems"


Funding Acknowledgements

This work was supported by the Australian Research Council Linkage Project LP150100892 "Generating Virtual Deployment Environments for Enterprise Software Systems."