Disentangling Multi-View Scanning in Mamba for Network Traffic Anomaly Detection
Network Traffic Anomaly Detection (NTAD) is a critical task in cybersecurity, yet timely and accurate anomaly detection remains challenging. Mamba has emerged as a particularly promising backbone for NTAD due to its linear-time complexity for long-sequence modeling. It further incorporates a dedicated multi-view scanning mechanism to enhance detection precision through complementary contextual cues. However, we identify a previously overlooked structural deficiency in multi-view Mamba scanning for NTAD: redundancy accumulation. Specifically, distinct scanning branches capture substantial view-invariant information, which is repeatedly amplified during multi-view fusion; conversely, view-s...
| Authors | Xinglin Lian, Chengtai Cao, Ting Zhong et al. |
|---|---|
| Primary category | cs.AI |
| Published | 24 July 2026 |
| Last updated at source | 24 July 2026 |
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