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

ABSTRACT: As IoT deployments grow in scale for applications such as smart cities, they
face increasing cyber-security threats. In particular, as evidenced by the
famous Mirai incident and other ongoing threats, large-scale IoT device
networks are particularly susceptible to being hijacked and used as botnets to
launch distributed denial of service (DDoS) attacks. Real large-scale datasets
are needed to train and evaluate the use of machine learning algorithms such as
deep neural networks to detect and defend against such DDoS attacks. We present
a dataset from an urban IoT deployment of 4060 nodes describing their
spatio-temporal activity under benign conditions. We also provide a synthetic
DDoS attack generator that injects attack activity into the dataset based on
tunable parameters such as number of nodes attacked and duration of attack. We
discuss some of the features of the dataset. We also demonstrate the utility of
the dataset as well as our synthetic DDoS attack generator by using them for
the training and evaluation of a simple multi-label feed-forward neural network
that aims to identify which nodes are under attack and when.
As IoT deployments grow in scale for applications such as smart cities, they
face increasing cyber-security threats. In particular, as evidenced by the
famous Mirai incident and other ongoing threats, large-scale IoT device
networks are particularly susceptible to being hijacked and used as botnets to
launch distributed denial of service (DDoS) attacks. Real large-scale datasets
are needed to train and evaluate the use of machine learning algorithms such as
deep neural networks to detect and defend against such DDoS attacks. We present
a dataset from an urban IoT deployment of 4060 nodes describing their
spatio-temporal activity under benign conditions. We also provide a synthetic
DDoS attack generator that injects attack activity into the dataset based on
tunable parameters such as number of nodes attacked and duration of attack. We
discuss some of the features of the dataset. We also demonstrate the utility of
the dataset as well as our synthetic DDoS attack generator by using them for
the training and evaluation of a simple multi-label feed-forward neural network
that aims to identify which nodes are under attack and when.
Dataset: Large-scale Urban IoT Activity Data for DDoS Attack Emulation
http://arxiv.org/abs/2110.01842v1