arXiv:2606.04957v1 Announce Type: cross Abstract: System-generated logs underpin security monitoring, yet their rigid template-based format hinders both automated analysis and human comprehension. We present NLLog (Natural-Language Log), a lightweight pipeline that deterministically rewrites parsed templates into WHO-WHAT-SEVERITY sentences, pools them with term-frequency-inverse-document-frequency weighting, classifies sessions with tree ensembles, and back-projects evidence with TreeSHAP for analyst review. On Hadoop Distributed File System (HDFS) and Blue Gene/L (BGL) corpora, NLLog exceeds
The proliferation of complex AI systems across critical infrastructure increases the need for sophisticated and interpretable security monitoring solutions.
This development offers a pathway to more effective and efficient cybersecurity operations, particularly in detecting anomalies within complex system logs using AI.
Traditional, rigid log analysis methods can be augmented or replaced by more human-interpretable, AI-driven approaches, improving incident response and security posture.
- · Cybersecurity sector
- · Organizations with complex IT infrastructure
- · Security Operations Centers (SOCs)
- · AI/ML in cybersecurity companies
- · Manual log analysis service providers
- · Legacy security monitoring solutions
Security analysts gain tools that convert machine-generated logs into understandable natural language, speeding up anomaly detection and investigation.
Improved anomaly detection leads to fewer successful breaches and reduced dwell times for attackers, increasing overall system resilience.
The widespread adoption of explainable AI in cybersecurity could set new industry standards for transparency and accountability in automated security systems.
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