@inproceedings{8d2b0cfcca0b40819d84c0373cf253d3,
title = "Detection of IoT botnet based on deep learning",
abstract = "In this paper, we propose a deep learning based approach for IoT botnet detection. We use the damped incremental statistics to extract basic traffic features of IoT devices and apply the Z-Score method to normalize the features. After that, the mangle area maps (TAM) based multivariate correlation analysis (MCA) algorithm is employed to generate dataset. Then we design a convolutional neural network (CNN) to learn the dataset and utilize the trained CNN to detect the traffic. The final experiments show that our approach can distinguish benign traffic and different kinds of attack traffic effectively and reaches the accuracy of 99.57%.",
keywords = "Attack detection, Convolutional neural network, Deep learning, IoT botnet, Multivariate correlation analysis",
author = "Junyi Liu and Shiyue Liu and Sihua Zhang",
note = "Publisher Copyright: {\textcopyright} 2019 Technical Committee on Control Theory, Chinese Association of Automation.; 38th Chinese Control Conference, CCC 2019 ; Conference date: 27-07-2019 Through 30-07-2019",
year = "2019",
month = jul,
doi = "10.23919/ChiCC.2019.8866088",
language = "English",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "8381--8385",
editor = "Minyue Fu and Jian Sun",
booktitle = "Proceedings of the 38th Chinese Control Conference, CCC 2019",
address = "United States",
}