There is an urgent need to reduce security risks related to the proliferation of Internet of Things (IoT) devices by detecting Distributed Denial-of-Service (DDoS) assaults as soon as possible. Immediate identification improves network security by stopping the botnet and assaults from spreading by quickly alerting and disconnecting infected IoT devices from the network. Botnet attack detection has seen a proliferation of methods, such as supervised and swarm-based algorithms, artificial neural networks, and others. This research compares many classifiers for botnet identification and finds that the decision tree classifier outperforms the state-of-the-art approaches. There is an urgent need to reduce security risks related to the proliferation of Internet of Things (IoT) devices by detecting Distributed Denial-of-Service (DDoS) assaults as soon as possible. Immediate identification improves network security by stopping the botnet and assaults from spreading by quickly alerting and disconnecting infected IoT devices from the network. Botnet attack detection has seen a proliferation of methods, such as supervised and swarm-based algorithms, artificial neural networks, and others. This research compares many classifiers for botnet identification and finds that the decision tree classifier outperforms the state-of-the-art approaches.