Nowadays, the IoT (internet of things) botnet has become a huge threat to network security. In response to this threat, we present a cooperative adaptive network intrusion detection system (IDS) framework with fog computing. The core part is cooperation detection architecture based on online adaptive machine learning algorithms. Our CAD-IDS aims to decrease the new attack detection time for all the IoT nodes and reduce the complexity of the cooperative IDS framework. The results of the experiment indicate that our CAD-IDS can detect IoT attacks efficiently and timely. Our ML code is available in https://github.com/LiQianchang/CAD-IDS-ML-Code.