In any network system, the intrusion is undesirable, and organizations are constantly searching for solutions that could effectively detect intrusion and, consequently, help them to react appropriately. However, packaged enterprise solutions  provided by industry-leading companies usually leave little room for optimization  and control in the hands of the users and sometimes incur costs that small- and  medium-sized organizations want to curtail, especially if the solutions are smart.  This work demonstrates how such organizations may build their home-grown Deep  Learning-based Intrusion Detection Systems (DL-IDSs) and integrate them into their  existing network. We have implemented an Intrusion Detection System for Small networks using deep learning architectures. The proposed system evaluated on UNSW-  NB dataset including more than 250,000 network packets and has obtained an accuracy of 89% in discriminating between abnormal and normal packets and 74%  for various nine network attack types classification.