Multi-View Aggregation in Graph Anomaly Detection: Scalability, F1 Scores, and Latency Trade-offs

This report synthesises findings from 10 peer-reviewed papers addressing the following research question: How does multi-view aggregation in graph anomaly detection frameworks affect the F1 score and inference latency when scaling from single-edge devices to distributed edge computing environments. Machine learning techniques are being widely used to develop an intrusion detection system (IDS) for detecting and classifying cyberattacks at the network-level and the host-level in a timely and automatic manner. However, many challenges arise since malicious attacks are. 10 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How does multi-view aggregation in graph anomaly detection frameworks affect the F1 score and inference latency when scaling from single-edge devices to distributed edge computing environments?

Autonomous literature synthesis. Automated review score: 8.5/10. Full text and citation available at Assignee Research.