OntoLogX: Ontology-Guided Knowledge Graph Extraction from Cybersecurity Logs with Large Language Models
In the realm of cybersecurity, system logs are a treasure trove of information, capturing critical data about attacker behaviors, exploited vulnerabilities, and malicious activities. However, the potential of these logs is often hindered by challenges such as a lack of structure, semantic inconsistencies, and fragmentation across various devices and sessions. To address these issues, researchers have developed OntoLogX, an innovative autonomous Artificial Intelligence (AI) agent designed to extract actionable Cyber Threat Intelligence (CTI) from raw logs.
Transforming Raw Logs into Knowledge Graphs
OntoLogX utilizes advanced Large Language Models (LLMs) to convert unstructured log data into ontology-grounded Knowledge Graphs (KGs). The process involves several key components:
- Lightweight Log Ontology: At the core of OntoLogX is a streamlined log ontology that provides a structured framework for interpreting log data.
- Retrieval Augmented Generation (RAG): This technique enhances the generation of KGs by retrieving relevant information that aids in producing accurate and contextually relevant outputs.
- Iterative Correction Steps: To ensure the generated KGs are both syntactically and semantically valid, OntoLogX employs iterative correction mechanisms.
These features together empower OntoLogX to effectively aggregate KGs into sessions, enabling comprehensive event-level analysis. Furthermore, the system employs LLMs to predict MITRE ATT&CK tactics, which are essential for linking low-level log evidence to higher-level adversarial objectives. This capability significantly enhances the depth of analysis possible from raw logs, transforming them into actionable insights.
Evaluation and Results
The efficacy of OntoLogX has been evaluated using two distinct datasets: logs from a public benchmark and a real-world honeypot dataset. The evaluation demonstrated robust KG generation across multiple backend systems, showcasing the versatility and reliability of the approach.
- KG Generation: OntoLogX successfully generated coherent and structured KGs from both datasets, highlighting its ability to manage the inherent noise and heterogeneity of log data.
- Accurate Mapping: The system achieved notable accuracy in mapping adversarial activities to MITRE ATT&CK tactics, effectively bridging the gap between raw log evidence and strategic threat frameworks.
- Precision and Recall: Results underscored the benefits of retrieval and correction methods employed by OntoLogX, enhancing both precision and recall in the analysis process.
Conclusion
OntoLogX represents a significant advancement in the field of cybersecurity, providing a powerful tool for extracting actionable CTI from complex log data. The integration of ontology-guided representations with state-of-the-art LLMs allows for a more structured and meaningful analysis of logs, ultimately aiding organizations in their efforts to combat cyber threats. As the landscape of cybersecurity continues to evolve, tools like OntoLogX will be crucial in enhancing the understanding and mitigation of adversarial activities.
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