The number of Distributed Denial of Service (DDoS) attacks using IoT devices has increased in recent years. Reasons for this growth include the security limitations of IoT devices, the number of devices, and their geographic distribution. Developing mechanisms to detect and mitigate DDoS attacks in this scenario is a current challenge in the area of network security. In the literature review, it is seen that recent academic works still tries to find the best way to combat this type of threat, with proposals that need to be tested against modern datasets that contain a diversity of modern denial of service attacks. This work proposes a detection module for an IoT controller that uses Machine Learning techniques to classify network traffic. The system was designed in the Software-Defined Networks (SDN) context and evaluated on an emulated platform using three actual and well-know datasets present in the literature. The results, at a sampling rate of 20% of network traffic, show a high precision, above 93%, a low false alarm rate, and detection rate of attacks above 96%, using a low profile emulated device.

Smart Detection-IoT: A DDoS Sensor System for Internet of Things

technical paper

The 27th International Conference on Systems, Signals and Image Processing.

Francisco Sales de Lima Filho

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Smart Detection-IoT: A DDoS Sensor System for Internet of Things

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