  Internet of Things (IoT) brings new challenges to the security solutions of
computer networks. So far, intrusion detection system (IDS) is one of the
effective security tools, but the vast amount of data that is generated by
heterogeneous protocols and "things" alongside the constrained resources of the
hosts, make some of the present IDS schemes defeated. To grant IDSs the ability
of working in the IoT environments, in this paper, we propose a new distributed
dimension reduction scheme which addresses the limited resources challenge. A
novel autoencoder (AE) designed, and it learns to generate a latent space.
Then, the constrained hosts/probes use the generated weights to lower the
dimension with a single operation. The compressed data is transferred to a
central IDS server to verify the traffic type. This scheme aims to lower the
needed bandwidth to transfer data by compressing it and also reduce the
overhead of the compression task in the hosts. The proposed scheme is evaluated
on three well-known network traffic datasets (UNSW-NB15, TON\_IoT20 and
NSL-KDD), and the results show that we can have a 3-dimensional latent space
(about 90\% compression) without any remarkable fall in IDS detection accuracy.
