  Cyberattacks are a major issues and it causes organizations great financial,
and reputation harm. However, due to various factors, the current network
intrusion detection systems (NIDS) seem to be insufficent. Predominant NIDS
identifies Cyberattacks through a handcrafted dataset of rules. Although the
recent applications of machine learning and deep learning have alleviated the
enormous effort in NIDS, the security of network data has always been a prime
concern. However, to encounter the security problem and enable sharing among
organizations, Federated Learning (FL) scheme is employed. Although the current
FL systems have been successful, a network's data distribution does not always
fit into a single global model as in FL. Thus, in such cases, having a single
global model in FL is no feasible. In this paper, we propose a
Segmented-Federated Learning (Segmented-FL) learning scheme for a more
efficient NIDS. The Segmented-FL approach employs periodic local model
evaluation based on which the segmentation occurs. We aim to bring similar
network environments to the same group. Further, the Segmented-FL system is
coupled with a weighted aggregation of local model parameters based on the
number of data samples a worker possesses to further augment the performance.
The improved performance by our system as compared to the FL and centralized
systems on standard dataset further validates our system and makes a strong
case for extending our technique across various tasks. The solution finds its
application in organizations that want to collaboratively learn on diverse
network environments and protect the privacy of individual datasets.
