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Network Flow based IoT Botnet Attack Detection using Deep Learning | IEEE Conference Publication | IEEE Xplore

Network Flow based IoT Botnet Attack Detection using Deep Learning


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 d...Show More

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.
Date of Conference: 06-09 July 2020
Date Added to IEEE Xplore: 10 August 2020
ISBN Information:
Conference Location: Toronto, ON, Canada

References

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