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Deception technologies like honeypots produce comprehensive log reports, butoften lack interoperability with EDR and SIEM technologies. A key bottleneck isthat existing information transformation plugins perform well on static logs(e.g. geolocation), but face limitations when it comes to parsing dynamic logtopics (e.g. user-generated content). In this paper, we present a run-timesystem (GPT-2C) that leverages large pre-trained models (GPT-2) to parsedynamic logs generate by a Cowrie SSH honeypot. Our fine-tuned model achieves89\% inference accuracy in the new domain and demonstrates acceptable executionlatency.

GPT-2C: A GPT-2 parser for Cowrie honeypot logs