Anomaly Detection via Federated Learning

• URL: http://arxiv.org/abs/2210.06614v1
• Date: Wed, 12 Oct 2022 22:40:29 GMT
• Title: Anomaly Detection via Federated Learning
• Authors: Marc Vucovich, Amogh Tarcar, Penjo Rebelo, Narendra Gade, Ruchi Porwal, Abdul Rahman, Christopher Redino, Kevin Choi, Dhruv Nandakumar, Robert Schiller, Edward Bowen, Alex West, Sanmitra Bhattacharya, Balaji Veeramani
• Abstract summary: We propose a novel anomaly detector via federated learning to detect malicious network activity on a client's server. 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.
• Score: 3.0755847416657613
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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