Insider threats pose significant security risks in distributed networks, because employees within the organisation may misuse their access to compromise systems. Centralised Machine Learning (ML) techniques are inappropriate in these situations due to privacy and data heterogeneity concerns. To address class imbalance and non-IID data, this study introduces FedAT, a Federated Adversarial Training that integrates federated learning (FL) with generative models to deliver privacy-preserving, multiclass Insider Threat Detection (ITD). FedAT outperforms centralized and conventional FL techniques in terms of scalability, privacy preservation, and detection accuracy, according to evaluations conducted on public CERT datasets.

Recently, the link prediction methods based on enclosing subgraph extraction and line graph transformation have been proven to achieve excellent prediction accuracy, but there are still some shortcomings, for examples, the time and space complexity of line graph transformation is too high and the graph neural network it used ignores the high-order relationship and local clustering structure between nodes, which makes it difficult to be widely used in real life and may affect the prediction accuracy. To solve the above problems, a hypergraph neural network model based on enclosing subgraph extraction is proposed, which converts subgraph into hypergraph by dual hypergraph transformation, and uses the hypergraph convolutional neural network to learn the higher-order features of nodes and edges respectively. After three experiments, the results show that the proposed model not only has higher prediction accuracy, but also has shorter runtime and less memory usage.

Summary In the age of the Internet of Things (IoT), large numbers of sensors and edge devices are deployed in various application scenarios; Therefore, collaborative learning is widely used in IoT to implement crowd intelligence by inviting multiple participants to complete a training task. As a collaborative learning framework, federated learning is designed to preserve user data privacy, where participants jointly train a global model without uploading their private training data to a third party server. Nevertheless, federated learning is under the threat of poisoning attacks, where adversaries can upload malicious model updates to contaminate the global model. To detect and mitigate poisoning attacks in federated learning, we propose a poisoning defense mechanism, which uses generative adversarial networks to generate auditing data in the training procedure and removes adversaries by auditing their model accuracy. Experiments conducted on two well‐known datasets, MNIST and Fashion‐MNIST, suggest that federated learning is vulnerable to the poisoning attack, and the proposed defense method can detect and mitigate the poisoning attack.