Omkar2118/datasets huggingface dataset:
IoT Network Intrusion Dataset

## 1. DATASET
We created various types of network attacks in Internet of Things (IoT) environment for academic purpose. Two typical smart home devices -- SKT NUGU (NU 100) and EZVIZ Wi-Fi Camera (C2C Mini O Plus 1080P) -- were used. All devices, including some laptops or smart phones, were connected to the same wireless network. The dataset consists of 42 raw network packet files (pcap) at different time points.

* The packet files are captured by using monitor mode of wireless network adapter. The wireless headers are removed by Aircrack-ng.

* All attacks except Mirai Botnet category are the packets captured while simulating attacks using tools such as Nmap. The case of Mirai Botnet category, the attack packets were generated on a laptop and then manipulated to make it appear as if it originated from the IoT device.



Revision History

* Three more packet files of Mirai Botnet -- Host Discovery and Telnet Bruteforce -- were added on September 20, 2019.



## 2. SUMMARY OF OUR DATASET

## 3. DOWNLOADS
You can download our dataset from IEEE dataport page: https://ieee-dataport.org/open-access/iot-network-intrusion-dataset



## 4. CİTATİON 
Please cite our dataset's page when you use this dataset as follows. 

Hyunjae Kang, Dong Hyun Ahn, Gyung Min Lee, Jeong Do Yoo, Kyung Ho Park, and Huy Kang Kim, "IoT Network Intrusion Dataset.", http://ocslab.hksecurity.net/Datasets/iot-network-intrusion-dataset, 2019

or 

Hyunjae Kang, Dong Hyun Ahn, Gyung Min Lee, Jeong Do Yoo, Kyung Ho Park, and Huy Kang Kim, "IoT network intrusion dataset", IEEE Dataport, 2019. [Online]. Available: http://dx.doi.org/10.21227/q70p-q449. Accessed: Sep. 30, 2019.



## 5. CONTACT
Hyunjae Kang (trifle19@korea.ac.kr) or Huy Kang Kim (cenda@korea.ac.kr)



## 6. SEE ALSO
See also our another dataset containing IoT traffic: IoT-Environment-Dataset

Please read [IoT-Environment-Dataset > 1.3 Comparison with IoT Network Intrusion Dataset] for more details.


cenda at korea.ac.kr  |  로봇융합관 304   |    +82-2-3290-4898
SZ-Taxi
Full name: Shenzhen Taxi Speed

Taxi speed data in 15min interval from 156 sensors on major roads of Luohu District in Shenzhen, China, from Jan. 1 to Jan. 31, 2015.

Introducing paper: T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction
UCCS
Full name: UnConstrained College Students Dataset

Unconstrained Face Detection and Open-Set Face Recognition Challenge

* Paper: [https://arxiv.org/abs/1708.02337](https://arxiv.org/abs/1708.02337)
* Official website: [https://vast.uccs.edu/Opensetface](https://vast.uccs.edu/Opensetface/)
* UnOfficial website: [https://exposing.ai/uccs](https://exposing.ai/uccs/)

Face detection and recognition benchmarks have shifted toward more difficult environments. The challenge presented in this paper addresses the next step in the direction of automatic detection and identification of people from outdoor surveillance cameras. While face detection has shown remarkable success in images collected from the web, surveillance cameras include more diverse occlusions, poses, weather conditions and image blur. Although face verification or closed-set face identification have surpassed human capabilities on some datasets, open-set identification is much more complex as it needs to reject both unknown identities and false accepts from the face detector. We show that unconstrained face detection can approach high detection rates albeit with moderate false accept rates. By contrast, open-set face recognition is currently weak and requires much more attention.