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Smart city article details

Title	Network Flow Based Iot Botnet Attack Detection Using Deep Learning
ID_Doc	38988
Authors	Sriram S.; Vinayakumar R.; Alazab M.; Soman K.P.
Year	2020
Published	IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020
DOI	http://dx.doi.org/10.1109/INFOCOMWKSHPS50562.2020.9162668
Abstract	Governments around the globe are promoting smart city applications to enhance the quality of daily-life activities in urban areas. Smart cities include internet-enabled devices that are used by applications like health care, power grid, water treatment, traffic control, etc to enhance its effectiveness. The expansion in the quantity of Internet-of-things (IoT) based botnet attacks is due to the growing trend of Internet-enabled devices. To provide advanced cyber security solutions to IoT devices and smart city applications, this paper proposes a deep learning (DL) based botnet detection system that works on network traffic flows. The botnet detection framework collects the network traffic flows, converts them into connection records and uses a DL model to detect attacks emanating from the compromised IoT devices. To determine an optimal DL model, many experiments are conducted on well-known and recently released benchmark data sets. Further, the datasets are visualized to understand its characteristics. The proposed DL model outperformed the conventional machine learning (ML) models. © 2020 IEEE.
Author Keywords	Big Data; Botnet; Cyber Security; Deep Learning; Internet of Things; Machine Learning; Smart Cities

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