A wrapper method based on a modified two-step league championship algorithm for detecting botnets in IoT environments.

Today, the Internet of Things (IoT) is extending due to a wide range of applications and services. The variety of devices connected to the internet, the discussion of security on these networks is a challenging issue. Security includes diverse aspects such as botnets. Botnets are a set of devices such as smartphones, computers, and others polluted by a program. This program, which is a bot herder, performs many deleterious operations and leads to various anomalies in the network. Identifying botnets due to their unique complexity is one of the main challenges in IoT security. In this paper, we propose a model for identifying botnets in the internet of things. The proposed method is based on selecting features using the modified League Championship Algorithm (LCA) and constructing the model using artificial neural networks. Feature selection speeds up the learning process and increases the resolution of botnets. The proposed method is simulated using MATLAB. The results reveal that the proposed method can make a better detection method than other schemes, and our modified version selects an optimal subset of features. As a result, it is an efficient model. [ABSTRACT FROM AUTHOR]