FadMan: Federated Anomaly Detection across Multiple Attributed Networks

Abstract

Anomaly subgraph detection has been widely used in various applications, ranging from cyber attack in computer networks to malicious activities in social networks. Despite an increasing need for federated anomaly detection across multiple attributed networks, only a limited number of approaches are available for this problem. Federated anomaly detection faces two major challenges. One is that isolated data in most industries are restricted share with others for data privacy and security. The other is most of the centralized approaches training based on data integration. The main idea of federated anomaly detection is aligning private anomalies from local data owners on the public anomalies from the attributed network in the server through public anomalies to federate local anomalies. In each private attributed network, the detected anomaly subgraph is aligned with an anomaly subgraph in the public attributed network. The significant public anomaly subgraphs are selected for federated private anomalies while preventing local private data leakage. The proposed algorithm FadMan is a vertical federated learning framework for public node aligned with many private nodes of different features, and is validated on two tasks -correlated anomaly detection on multiple attributed networks and anomaly detection on an attributeless network -using five real-world datasets. In the first scenario, FadMan outperforms competitive methods by at least 12% accuracy at 10% noise level. In the second scenario, by analyzing the distribution of abnormal nodes, we find that the nodes of traffic anomalies are associated with the event of postgraduate entrance examination on the same day. 1 In this paper, we use 'graph ' and 'network', 'vertex' and 'node', and 'attribute' and 'feature' interchangeably. Preprint. Under review.

Anomaly detection and defense techniques in federated learning: a comprehensive review

“…suggested a method for detecting spectral anomalies that involves embedding both original and poisoned samples in a low-latitude space and then finding the samples with significant deviations. Wu et al (2022) developed the "FadMan" algorithm, i.e., a vertical FL framework proven using five real-world datasets on two tasks (correlated anomaly detection on several attributed networks and anomaly detection on an attributeless network). It was designed for public nodes aligned with numerous private nodes with various features.…”