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