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As IoT deployments grow in scale for applications such as smart cities, theyface increasing cyber-security threats. In particular, as evidenced by thefamous Mirai incident and other ongoing threats, large-scale IoT devicenetworks are particularly susceptible to being hijacked and used as botnets tolaunch distributed denial of service (DDoS) attacks. Real large-scale datasetsare needed to train and evaluate the use of machine learning algorithms such asdeep neural networks to detect and defend against such DDoS attacks. We presenta dataset from an urban IoT deployment of 4060 nodes describing theirspatio-temporal activity under benign conditions. We also provide a syntheticDDoS attack generator that injects attack activity into the dataset based ontunable parameters such as number of nodes attacked and duration of attack. Wediscuss some of the features of the dataset. We also demonstrate the utility ofthe dataset as well as our synthetic DDoS attack generator by using them forthe training and evaluation of a simple multi-label feed-forward neural networkthat aims to identify which nodes are under attack and when.

Dataset: Large-scale Urban IoT Activity Data for DDoS Attack Emulation