Title: Security in the internet of things: botnet detection in software-defined networks by deep learning techniques
Authors: Ivan Letteri; Giuseppe Della Penna; Giovanni De Gasperis
Addresses: Department of Information Engineering, Computer Science and Mathematics, University of L'Aquila, L'Aquila, Italy ' Department of Information Engineering, Computer Science and Mathematics, University of L'Aquila, L'Aquila, Italy ' Department of Information Engineering, Computer Science and Mathematics, University of L'Aquila, L'Aquila, Italy
Abstract: The diffusion of the internet of things (IoT) is making cyber-physical smart devices an element of everyone's life, but also exposing them to malware designed for conventional web applications, such as botnets. Botnets are one of the most widespread and dangerous malware, so their detection is an important task. Many works in this context make use of general malware detection techniques and rely on old or biased traffic samples, making their results not completely reliable. Moreover, software-defined networking (SDN), which is increasingly replacing conventional networking especially in the IoT, limits the features that can be used to detect botnets. We propose a botnet detection methodology based on deep learning techniques, tested on a new, SDN-specific dataset with a high (up to 97%) classification accuracy. Our algorithms have been implemented on two state-of-the-art frameworks, i.e., Keras and TensorFlow, so we are confident that our results are reliable and easily reproducible.
Keywords: cyber-physical devices; internet of things; IoT; software-defined networking; SDN; botnet detection; machine learning; neural networks; deep learning; network security.
International Journal of High Performance Computing and Networking, 2019 Vol.15 No.3/4, pp.170 - 182
Received: 03 Dec 2018
Accepted: 07 Apr 2019
Published online: 30 Mar 2020 *