Abstract— The use of IoT devices has grown rapidly, leading
to an increase in cyber attacks that pose greater security and
privacy threats than ever before. One such threat is botnet attacks
on IoT devices. An IoT botnet is a group of Internet-connected IoT
devices infected with malware and remotely controlled by an
attacker. Machine learning techniques can be employed to detect
botnet attacks. The use of machine learning-based detection
methods has been shown to be effective in identifying cyber
attacks. The performance of the detection system in machine
learning can be improved by utilizing data reduction methods. The
data reduction process in classification is used to overcome the
problem of scalability and computation resources in the IoT. This
paper proposes a detection system using the Autoencoder
reduction method and the Decision tree classification method. The
test results demonstrate that the Deep Autoencoder algorithm can
reduce data and memory usage from 1.62 GB to 75.9 MB, while
also improving the performance of decision tree classification,
resulting in a high level of accuracy up to 100%. The Autoencoder
approach in conjunction with the Decision Tree exhibits superior
capabilities compared to previous studies.