With the rapid growth of threats and diversity in the manner of attack, Internet of things (IoT) systems has major challenges in providing methods to detect security vulnerabilities and attacks. There have been increasing developments of many detection tools and methods using full-time series data during malware execution based on machine learning/deep learning. However, the effectiveness of existing works is tightly bound by the requirement to use full-time series data. On the other hand, an earlier detection would help propose better solutions to respond to the IoT Botnet. Therefore, it mitigating the damage from potential attacks. In this paper, going beyond the full-time series data-based methods, we propose a collaborative machine learning model to effectively automate the early detection of IoT Botnet based on many features. The proposed model is 99.37% accurate on a dataset of 5023 IoT botnet and 3888 benign samples.