Insight-LLM: LLM-enhanced Multi-view Fusion in Insider Threat Detection

• URL: http://arxiv.org/abs/2509.01509v1
• Date: Mon, 01 Sep 2025 14:31:12 GMT
• Title: Insight-LLM: LLM-enhanced Multi-view Fusion in Insider Threat Detection
• Abstract summary: We present Insight-LLM, the first modular multi-view fusion framework specifically tailored for insider threat detection.<n> Insight-LLM employs frozen, pre-nes, achieving state-of-the-art detection with low latency and parameter overhead.
• Score: 9.040775027499265
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Insider threat detection (ITD) requires analyzing sparse, heterogeneous user behavior. Existing ITD methods predominantly rely on single-view modeling, resulting in limited coverage and missed anomalies. While multi-view learning has shown promise in other domains, its direct application to ITD introduces significant challenges: scalability bottlenecks from independently trained sub-models, semantic misalignment across disparate feature spaces, and view imbalance that causes high-signal modalities to overshadow weaker ones. In this work, we present Insight-LLM, the first modular multi-view fusion framework specifically tailored for insider threat detection. Insight-LLM employs frozen, pre-nes, achieving state-of-the-art detection with low latency and parameter overhead.

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