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Publication Type : Conference Paper
Publisher : IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE
Source : IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE, Toronto, ON, Canada (2020)
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Year : 2020
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.
Cite this Research Publication : S. Sriram, Vinayakumar, R., Alazab, M., and Dr. Soman K. P., “Network Flow based IoT Botnet Attack Detection using Deep Learning”, in IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Toronto, ON, Canada, 2020.