Anomaly Detection via Federated Learning

Machine learning has helped advance the field of anomaly detection by incorporating classifiers and autoencoders to decipher between normal and anomalous behaviour. Additionally, federated learning has provided a way for a global model to be trained with multiple clients” data without requiring the client to directly share their data. This paper proposes a novel anomaly detector via federated learning to detect malicious network activity on a client's server. In our experiments., we use an autoencoder with a classifier in a federated learning framework to determine if the network activity is benign or malicious. By using FedSam., our novel min-max scalar and sampling technique., we created a federated learning framework that allows the global model to learn from heterogeneous clients and., in turn., provide a means for each client to improve their intrusion detection system's defense against cyber-attacks.