FedSA: Accelerating Intrusion Detection in Collaborative Environments with Federated Simulated Annealing. (arXiv:2205.11519v1 [cs.CR])

Fast identification of new network attack patterns is crucial for improving
network security. Nevertheless, identifying an ongoing attack in a
heterogeneous network is a non-trivial task. Federated learning emerges as a
solution to collaborative training for an Intrusion Detection System (IDS). The
federated learning-based IDS trains a global model using local machine learning
models provided by federated participants without sharing local data. However,
optimization challenges are intrinsic to federated learning. This paper
proposes the Federated Simulated Annealing (FedSA) metaheuristic to …

arxiv collaborative detection