HIDBench: Benchmarking Large Language Models for Host-Based Intrusion Detection
Recent benchmark efforts have advanced the evaluation of large language models (LLMs) in cybersecurity, including tasks such as penetration testing and vulnerability identification. However, a critical cybersecurity task, namely intrusion detection from system logs, remains unexplored. In this work, we present a new benchmark to assess LLMs' capabilities in supporting host-based intrusion detection systems (HIDS). This task requires fine-grained reasoning over large-scale, noisy, and highly imbalanced system logs, where complex interactions between benign and malicious activities make reliable detection challenging. Our benchmark unifies three public system log datasets, DARPA-E3, DARPA-E...
| Authors | Danyu Sun, Jinghuai Zhang, Yuan Tian et al. |
|---|---|
| Primary category | cs.CR |
| Published | 20 May 2026 |
| Last updated at source | 20 May 2026 |
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