Insight-LLM: LLM-enhanced Multi-view Fusion in Insider Threat Detection
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908514416328704 |
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| author | Song, Chengyu Zheng, Jianming |
| author_facet | Song, Chengyu Zheng, Jianming |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_01509 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Insight-LLM: LLM-enhanced Multi-view Fusion in Insider Threat Detection Song, Chengyu Zheng, Jianming Cryptography and Security 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. |
| title | Insight-LLM: LLM-enhanced Multi-view Fusion in Insider Threat Detection |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2509.01509 |