In the virtual world, most of the cyber-attacks are done by Botnet. The Botnet is one of the most versatile threats because it can be controlled from a remote place. Most of the existing Botnet detection approaches focused on binary classification based on traditional machine learning, and these have some limitations. In this paper, a Multiclass classification method has been proposed for Botnet detection based on Artificial Neural Networks with some variations. The proposed model is used to detect different types of Botnet from a large pool of Botnet families. This paper has used a dataset consisting of seven different classes to train and test the model. In this work, we got promising results in terms of accuracy, 99.04%, and other performance measures. The accuracy of the proposed is better when compared with other traditional machine learning models when evaluated using the same dataset.