Feature Analysis of IoT Botnet Attacks based on RNN and LSTM
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2020 by IJETT Journal|
|Year of Publication : 2020|
|Authors : Jiyeon Kim, Hyerin Won, Minsun Shim, Seungah Hong, Eunjung Choi
|DOI : 10.14445/22315381/IJETT-V68I4P208S|
MLA Style: Jiyeon Kim, Hyerin Won, Minsun Shim, Seungah Hong, Eunjung Choi "Feature Analysis of IoT Botnet Attacks based on RNN and LSTM" International Journal of Engineering Trends and Technology 68.4(2020):43-47.
APA Style:Jiyeon Kim, Hyerin Won, Minsun Shim, Seungah Hong, Eunjung Choi. Feature Analysis of IoT Botnet Attacks based on RNN and LSTM International Journal of Engineering Trends and Technology, 68(4),43-47.
As the number of IoT devices rapidly increases, attacks targeting IoT devices also increase. In the IoT environment, IoT devices are vulnerable to network attacks because IoT devices are connected to the network to process collected data through the internet. In order to detect IoT attacks, developing a security solution considering characteristics of various types of IoT devices is necessary. However, it is challenging to develop a customized security solution for each type of the IoT device. Especially, traditional rule-based detection techniques would trigger massive false alarms. In this paper, we employ deep learning (DL) techniques that train empirical data. We focus on botnet attacks targeting IoT devices and develop a DL-based botnet detection system using a Recurrent Neural Network (RNN) model as well as Long Short-Term Memory (LSTM) model. As a dataset, we use N-BaIoT which is generated by injecting botnet attacks into various types of IoT devices. We train the dataset based on the DL-based system and analyse features that improve performance of the botnet detection.
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Internet of Things, Attacks, Botnets, NBaIoT, Deep learning, Recurrent neural network, Long Short-Term Memory