Omkar2118/datasets huggingface dataset:
GoodsAD

The GoodsAD dataset contains 6124 images with 6 categories of common supermarket goods. Each category contains multiple goods. All images are acquired with 3000 × 3000 high-resolution. The object locations in the images are not aligned. Most objects are in the center of the images and one image only contains a single object. Most anomalies occupy only a small fraction of image pixels. Both image-level and pixel-level annotations are provided.

Each image is named with 6 digits, with the first three digits representing the category of the product and the last three representing the serial number. The dataset format is same as MVTec AD.

Introducing paper: PKU-GoodsAD: A Supermarket Goods Dataset for Unsupervised Anomaly Detection and Segmentation
IoT devices captures

# IoT devices captures

- [Samuel Marchal](https://research.aalto.fi/en/persons/samuel-marchal) (Creator)

## Description

This dataset represents the traffic emitted during the setup of 31 smart home IoT devices of 27 different types (4 types are represented by 2 devices each). Each setup was repeated at least 20 times per device-type.
 Each directory contains several pcap files, each representing a setup of the given device directory. Files are named Setup-X-Y-STA.pcap where X is the person realizing the setup and Y is the sequence number of the given capture. The file \_iotdevice-mac.txt contains the MAC address of the considered IoT device.

 Please refer to the following publication when citing this dataset:
 Markus Miettinen, Samuel Marchal, Ibbad Hafeez, N. Asokan, Ahmad-Reza Sadeghi, Sasu Tarkoma, "IoT Sentinel: Automated Device-Type Identification for Security Enforcement in IoT," in Proc. 37th IEEE International Conference on Distributed Computing Systems (ICDCS 2017), Jun. 2017.

| Date made available | 3 Apr 2017 |
| --- | --- |
| Publisher | [Aalto University](https://research.aalto.fi/en/datasets/iot-devices-captures) |
| Date of data production | 2016 |

## Dataset Licences

- Unspecified

## Contact person

- [Samuel Marchal](https://research.aalto.fi/en/persons/samuel-marchal)

-
## DOI

[10.24342/285a9b06-de31-4d8b-88e9-5bdba46cc161](https://doi.org/10.24342/285a9b06-de31-4d8b-88e9-5bdba46cc161)

## Access Dataset

- [captures\_IoT\_Sentinel.zip](https://research.aalto.fi/files/13004478/captures_IoT_Sentinel.zip)

File: multipart/x-zip, 25.4 MB

Type: Dataset

## Cite this

- **DataSetCite**

Marchal, S. (Creator) (3 Apr 2017). IoT devices captures. Aalto University. captures\_IoT\_Sentinel(.zip). 10.24342/285a9b06-de31-4d8b-88e9-5bdba46cc161

Introducing paper: IoT Sentinel: Automated Device-Type Identification for Security Enforcement in IoT
IoT Traffic Traces
Full name: IoT Traffic Traces - Data Collected for IEEE TMC 2018

# **IOT TRAFFIC TRACES**

# Data Collected for IEEE TMC 2018

Cite our data

A. Sivanathan, H. Habibi Gharakheili, F. Loi, A. Radford, C. Wijenayake, A. Vishwanath and V. Sivaraman, "Classifying IoT Devices in Smart Environments Using Network Traffic Characteristics", IEEE Transactions on Mobile Computing, Aug, 2018.

Post Processing Tools

[https://github.com/arunmir/sdn-sim](https://github.com/arunmir/sdn-sim)

Resources

[List\_Of\_Devices.txt](https://iotanalytics.unsw.edu.au/resources/List_Of_Devices.txt)
IoT Benign and Attack Traces
Full name: IoT Benign and Attack Traces -  Data Collected for ACM SOSR 2019

**IOT BENIGN AND ATTACK TRACES**

# Data Collected for ACM SOSR 2019

# Attack & Benign Data

# Instructions

[Flow data](https://iotanalytics.unsw.edu.au/anomaly-data/flowdata.zip) contains flow counters of MUD flow, each instance in the file are collected every one minute.
[Annotations](https://iotanalytics.unsw.edu.au/anomaly-data/annotations.zip) contains information about the start, end time of the attack and corresponsing MUD flows that are impacted through the Attack. More information about the device and the attacker can be found in [here](https://iotanalytics.unsw.edu.au/anomaly-data/attackinfo.xlsx)
 Below is an example of the annotations from the Samsung smart camera.
 eg: "1527838552,1527839153,Localfeatures|Arpfeatures,ArpSpoof100L2D"
 The above line indicates that the start time of the attack to be 1527838552 and end time is 1527839153. "Localfeatures|Arpfeatures" explains that it should impact the local communication and ARP protocol. "ArpSpoof100L2D" means that the attack was arpspoof lauched with the maximum rate of 100 packets per seconds. In order to identify the attack rows in flow stats you can use below condition.
 "if (flowtime \>= startTime\*1000 and endTime\*1000\>=flowtime) then attack = true" -- This corresponds to the line 4470 to 4479 in the samsung smart camera.

# Cite our data

A. Hamza, H. Habibi Gharakheili, T. Benson, V. Sivaraman, "Detecting Volumetric Attacks on IoT Devices via SDN-Based Monitoring of MUD Activity", ACM SOSR, San Jose, California, USA, Apr 2019.

# Source code

[https://github.com/ayyoob/mud-ie](https://github.com/ayyoob/mud-ie)

# Contact

[ayyoobhamza@student.unsw.edu.au](https://github.com/ayyoob/mud-ie)
Persian Font Recognition (PFR)

Persian Font Recognition (PFR)

A dataset in order to solve font recognition for the Persian language.
This dataset is part of a paper titled "Persis: A Persian Font Recognition Pipeline Using Convolutional Neural Networks".

If you use the findings of the paper or this dataset please cite the paper.


[GitHub repo](https://github.com/mehrdad-dev/persis)
Persian Text Image Segmentation (PTI SEG)

Persian Text Image Segmentation (PTI SEG)

This dataset is part of a paper titled "Persis: A Persian Font Recognition Pipeline Using Convolutional Neural Networks".

A dataset in order to solve image segmentation for the Persian texts. For generating the PTI SEG dataset, we use 735 different backgrounds in four types (stock, papers, noisy real world, and texture), and apply four effects (gradient light, folding, subtle noise, and ink bleed) to the image

If you use the findings of the paper or this dataset please cite the paper. [GitHub repo](https://github.com/mehrdad-dev/persis)