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New benchmark tests LLMs for intrusion detection in system logs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed HIDBench, a new benchmark designed to evaluate the effectiveness of large language models (LLMs) in host-based intrusion detection using system logs. The benchmark integrates three public datasets and a pipeline for processing raw telemetry into LLM-friendly formats, simulating realistic detection scenarios. Evaluations of leading LLMs showed significant performance variations, with models struggling with noisy and complex log data, indicating that while LLMs show promise for intrusion detection, their reliability is contingent on data complexity and robust system design. AI

IMPACT Establishes a new evaluation standard for LLMs in cybersecurity, highlighting current limitations in intrusion detection.

RANK_REASON The cluster contains an academic paper introducing a new benchmark for evaluating LLMs in a specific cybersecurity task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New benchmark tests LLMs for intrusion detection in system logs

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The cluster contains an academic paper introducing a new benchmark for evaluating LLMs in a specific cybersecurity task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Danyu Sun, Jinghuai Zhang, Yuan Tian, Zhou Li ·

    HIDBench: Benchmarking Large Language Models for Host-Based Intrusion Detection

    arXiv:2605.21773v1 Announce Type: cross Abstract: 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 i…

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