Detection of IoT Botnet Based on Convolutional Neural Network and Linear Support Vector Machine.

The development of machine learning has brought new methods for botnet detection. Traditional machine learning methods and deep learning methods are used in botnet detection, but the former requires prior knowledge to select features. And deep learning methods solve this problem. This paper proposes a new botnet detection model that combines Convolutional Neural Network (CNN) with Support Vector Machine (SVM). This approach directly acquires network traffic data, preprocesses the data to obtain input suitable for CNN, utilizes CNN for feature extraction after preprocessing, and feeds the extracted features through two convolutional layers. The obtained features are then input to a SVM for classification. This method leverages the powerful feature extraction capabilities of CNN and the faster computational speed of linear SVM, resulting in faster training and excellent classification performance. Experiments show that the method has good performance on botnet detection and reduces training time compared to the CNN model.