Internet-scale active NAT detections for IoT Devices

1.Decription: This dataset was generated by an experimental evalation of our research ( title: Detecting Internet-scale NATs for IoT Devices Based on Tri-net). Condersing the necessary of privacy protection and experimental reproduction, we randomly sampled one tenth of our total experimental resualts as this dataset. 2.Counts: The number of entries in this dataset is 864,461. Among which, each entry contains an entire NAT detection result labels of a online IoT device (details see label defination). 3.Label defination: The values of NAT label have three categories: 1 means NAT, -1 means non-NAT, 0 means unknown.N1: NAT pre-label of network layer;N2: NAT pre-label of transport layer;N3: NAT pre-laber of application layer;acknum: TCP acknowledgment number (an) of transport layer;banner: protocol banner is obtained (value as True) or not (value as False);deviceBrand: the brand of current IoT device;deviceCategory: the category of current IoT device (RSS and CSS means general router or switch device; VSS means monitor device; ICS means industrial device; OAS means printer device, unknown means other type of device);deviceModel: the model of current IoT device;deviceType: the detail type of current IoT device (such as IP camera, printer, firewall);icmp: ICMP type and code IDs of network layer;ip: public IP address of current IoT device;ipid: IP identification of network layer;port: port number of current IoT device;protocol: protocol type of current IoT device;sn: TCP sequence number (sn) of transport layer;ttl: Time to live (ttl) of network layer;ws: TCP window size (ws) of transport layer;other1: temporary label one (pseudo-Label);other2: temporary label two (pseudo-Label);label: final NAT detection result. 4.Data sources: The original public data was collected on Rapid7 website which contains the original values on network and transport layer, and the protocol banner of the IP and port on application layer.The brand, model and type was fingerprinted by our previous approach.The pre-labels were conducted based on the features of different dimensions on each layers, and these pre-labeled instances can be used as training dataset of Tri-net module. 5.Privacy protection and ethical statement: As our evalation has been implemented on Internet-scale online IoT devices, the public IP addresses are truly accessed on the Internet. Consequently, excepect of sampling one tenth of total results, we also processed the IP addresses for privacy protection. The last two numbers of the IP addresses were replaced as "*". For example, "192.168.101.102" was processed as "192.168.10*.10*". Even so, we still ethically claim that this dataset should only be used for research experiments. And we call upon all researchers not use the dataset for other motivations in duty. 6.Reusable value According to our test, the sampled dataset was still completely satisfy the demand of the realization experiment with both the performance experiment and the experiment to realize our approach based on Tri-net.