1. Anomaly Detection via Federated Learning
- Author
-
Vucovich, Marc, Tarcar, Amogh, Rebelo, Penjo, Gade, Narendra, Porwal, Ruchi, Rahman, Abdul, Redino, Christopher, Choi, Kevin, Nandakumar, Dhruv, Schiller, Robert, Bowen, Edward, West, Alex, Bhattacharya, Sanmitra, and Veeramani, Balaji
- Subjects
Computer Science - Machine Learning ,Computer Science - Artificial Intelligence ,Computer Science - Cryptography and Security - 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.
- Published
- 2022