Nov 16, 2022 · To detect insider threats with large and complex audit data, a Multi-Edge Weight Relational Graph Neural Network method (MEWRGNN) for robust ...
To detect insider threats with large and complex audit data, a Multi-Edge. Weight Relational Graph Neural Network method (MEWRGNN) for robust anomaly detection ...
A Multi-Edge Weight Relational Graph Neural Network method (MEWRGNN) for robust anomaly detection is proposed in this paper, which adopts several graph ...
Sep 1, 2023 · To detect insider threats with large and complex audit data, a Multi-Edge Weight Relational Graph Neural Network method (MEWRGNN) for robust ...
Jiang et al. [18] proposed a deep learning-based approach using Graph Convolutional Networks (GCN) to detect anomalous behaviors of users posing possible ...
Chen, and D. Li, "Robust Anomaly-Based Insider Threat Detection Using Graph Neural Network," IEEE Transactions on Network and Service Management, vol. 20, no.
Anomaly detection with graph convolutional networks for insider threat and fraud detection ... based sentiment and network for insider threats detection.
Apr 30, 2024 · In this paper, we propose Log2Graph, a novel insider threat detection method based on graph convolution neural network. To achieve efficient ...
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Mar 17, 2024 · In this paper, we conduct the first study towards real-time ITD at activity level, and present a fine-grained and efficient framework LAN.
Apr 25, 2024 · ... detection system for in-vehicle network by graph neural network ... Robust Anomaly-Based Insider Threat Detection Using Graph Neural Network.
Stop chasing false positives. Building defensible cases with contextualized true threats. Get...