Serial Number: 44
Year: 2016
Kind of Traffic: Real
Publicly Available: Yes
Count of Records: 4060 nodes
Features Count: 3
No. of citations: 12
Attack Type: DDOS
Download Links: Not Available
Abstract: Hekmati and colleagues emphasized that effectively detecting Distributed Denial of Service (DDoS) attacks constitutes a crucial step in their prevention. The prospective creation of a NN model for detection is expected to pose challenges, given the widespread infiltration of IoT networks by malicious entities and the complex nature of botnet attacks. The availability of substantial and pertinent datasets are pivotal, yet many existing datasets do not specifically address environments of IoT. To address this deficiency, the researchers introduced an urban-IoT DDOS-dataset obtained from a substantial real-world experiment conducted in an urban Internet of Things (IoT) system within a significant city. This dataset comprises 4060 spatially distributed sensors activated by events. The recorded data encompasses the binary activity status of each node, documented at 30-second intervals over the course of a month. Furthermore, three metadata fields such as timestamps for the node's activity status, geolocation (longitude and latitude) of the node, and node ID are included. The researchers also provided a script for generating attacks within this dataset. It's important to note that alterations in node activity status are recorded as "zero" for normal activity, and "one" for occurrences of attacks. Both the script for generating attacks and the dataset are available in CSV format.