A Comparative Study: Effect of Feature Selection Methods on Detecting Botnet Attacks in IoT Devices by Using Deep Learning
Abstract
The Internet of Things, or IoT, is now an important technology that is the basis for various innovations
in intelligent environments, including smart homes and innovative healthcare. Due to their architecture,
many IoT devices suffer from security issues, leading to increased electronic threats targeting IoT
devices, facilitating abuse and lack of security control. The most common attack in IoT environments is
the botnet attack, which supports various criminal activities. In this study, This article aims to study
different methods for selecting features and comparing them to find the best possible strategies for
selecting and reducing features to detect botnet attacks in IoT devices. By using the UNSW-NB15
dataset, we will analyze the system's performance suggested to solve the classification issue. On the
UNSW-NB15 dataset, the results obtained using the LSTM, BRNN, and GRU classifiers were analyzed
to solve the binary classification issue and multiclass classification issues and compare the performance
of three different selection features (Correlation method, GNDO method, and Lasso method) of network
intrusion detection. The proposed system was evaluated by using different performance metrics and
comparing the various techniques to show better performance. The results showed with that a Filter
method (Correlation) for selecting features is better than other methods, and the model GRU in deep
learning got the highest accuracy, amounting to 92.71% and 78.62% for both binary and multiple
classifications, respectively. This study can potentially be applied in practical settings to detect real-time
network intrusions with a dynamic nature.
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