Anomaly Detection via Federated Learning
Abstract
Machine learning has helped advance the field of anomaly detection by incorporating classifiers and autoencoders to decipher between normal and anomalous behavior. 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 our novel min-max scalar and sampling technique, called FedSam, we determined federated learning allows the global model to learn from each client's data and, in turn, provide a means for each client to improve their intrusion detection system's defense against cyber-attacks.
Cite
@article{arxiv.2210.06614,
title = {Anomaly Detection via Federated Learning},
author = {Marc Vucovich and Amogh Tarcar and Penjo Rebelo and Narendra Gade and Ruchi Porwal and Abdul Rahman and Christopher Redino and Kevin Choi and Dhruv Nandakumar and Robert Schiller and Edward Bowen and Alex West and Sanmitra Bhattacharya and Balaji Veeramani},
journal= {arXiv preprint arXiv:2210.06614},
year = {2022}
}