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 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 bet
The rapid advancements in large language models necessitate their application and evaluation in critical cybersecurity domains like intrusion detection, reflecting a natural progression in AI capabilities.
Evaluating LLMs for host-based intrusion detection is crucial for enhancing cybersecurity defenses, potentially automating and improving the accuracy of identifying sophisticated threats.
The ability of LLMs to analyze complex system logs for intrusion detection shifts from theoretical potential to a benchmarked, practical application, changing how cybersecurity tools might be developed.
- · Cybersecurity firms
- · Organizations with advanced threat landscapes
- · AI/ML developers
- · Traditional HIDS vendors resistant to AI integration
- · Hackers relying on obfuscation
- · Security teams with limited AI expertise
LLMs will become an integral part of next-generation host-based intrusion detection systems, leading to more robust and adaptive security.
The improved detection capabilities will likely increase the cost and complexity for malicious actors to successfully compromise systems.
This could lead to an arms race in cyber warfare, where both offensive and defensive strategies become heavily reliant on advanced AI systems.
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Read at arXiv cs.LG