1. TUANDROMD ( Tezpur University Android Malware Dataset): TUNADROMD dataset contains 4465 instances and 241 attributes. The target attribute for classification is a category (malware vs goodware). (N.B. This is the preprocessed version of TUANDROMD)
2. REWEMA: REWEMA (Retrieval of 32-bit Windows Architecture Executables Applied to Malware Analysis) can be used in Artificial intelligence-based antivirus.
3. REJAFADA : REJAFADA (Retrieval of Jar Files Applied to Dynamic Analysis) aims to be used, as benchmark, to check the quality of the detection of Jar malware.
4. TTC-3600: Benchmark dataset for Turkish text categorization: The TTC-3600 data set is a collection of Turkish news and articles including categorized 3,600 documents from 6 well-known portals in Turkey. It has 4 different forms in ARFF Weka format.
5. IDA2016Challenge: The dataset consists of data collected from heavy Scania trucks in everyday usage.
6. Gisette: GISETTE is a handwritten digit recognition problem. The problem is to separate the highly confusible digits '4' and '9'. This dataset is one of five datasets of the NIPS 2003 feature selection challenge.
7. Semeion Handwritten Digit: 1593 handwritten digits from around 80 persons were scanned, stretched in a rectangular box 16x16 in a gray scale of 256 values.
8. Physical Unclonable Functions: The dataset is generated from Physical Unclonable Functions (PUFs) simulation, specifically XOR Arbiter PUFs. PUFs are used for authentication purposes. For more info, refer to our paper below.
9. Opinion Corpus for Lebanese Arabic Reviews (OCLAR): Opinion Corpus for Lebanese Arabic Reviews (OCLAR) corpus is utilizable for Arabic sentiment classification on services’ reviews, including hotels, restaurants, shops, and others.
10. YouTube Multiview Video Games Dataset: This dataset contains about 120k instances, each described by 13 feature types, with class information, specially useful for exploring multiview topics (cotraining, ensembles, clustering,..).
11. Malware static and dynamic features VxHeaven and Virus Total: 3 datasets: staDynBenignLab.csv, features extracted from 595 files (Win 7 and 8); staDynVxHeaven2698Lab.csv, from 2698 files of VxHeaven and staDynVt2955Lab.csv,from 2955 files of Virus Total.
12. UJIIndoorLoc: The UJIIndoorLoc is a Multi-Building Multi-Floor indoor localization database to test Indoor Positioning System that rely on WLAN/WiFi fingerprint.
13. Multiple Features: This dataset consists of features of handwritten numerals (`0'--`9') extracted from a collection of Dutch utility maps
14. Character Font Images: Character images from scanned and computer generated fonts.
15. URL Reputation: Anonymized 120-day subset of the ICML-09 URL data containing 2.4 million examples and 3.2 million features.
16. Daily and Sports Activities: The dataset comprises motion sensor data of 19 daily and sports activities each performed by 8 subjects in their own style for 5 minutes. Five Xsens MTx units are used on the torso, arms, and legs.
17. Kitsune Network Attack Dataset: A cybersecurity dataset containing nine different network attacks on a commercial IP-based surveillance system and an IoT network. The dataset includes reconnaissance, MitM, DoS, and botnet attacks.
18. Deepfakes: Medical Image Tamper Detection: Medical deepfakes: CT scans of human lungs, where some have been tampered with cancer added/removed. Can you find them?
19. Swarm Behaviour: This dataset achieved from an online survey, which is run by UNSW, Australia. It contains three data of ' Flocking - Not Flocking', 'Aligned - Not Aligned', and 'Grouped - Not Grouped'.
20. Gas Sensor Array Drift Dataset at Different Concentrations: This archive contains 13910 measurements from 16 chemical sensors exposed to 6 different gases at various concentration levels.
21. REALDISP Activity Recognition Dataset: The REALDISP dataset is devised to evaluate techniques dealing with the effects of sensor displacement in wearable activity recognition as well as to benchmark general activity recognition algorithms
22. ISOLET: Goal: Predict which letter-name was spoken--a simple classification task.
23. Smartphone Dataset for Human Activity Recognition (HAR) in Ambient Assisted Living (AAL): This data is an addition to an existing dataset on UCI. We collected more data to improve the accuracy of our human activity recognition algorithms applied in the domain of Ambient Assisted Living.
24. FMA: A Dataset For Music Analysis: FMA features 106,574 tracks and includes song title, album, artist, genres; play counts, favorites, comments; description, biography, tags; together with audio (343 days, 917 GiB) and features.
25. SECOM: Data from a semi-conductor manufacturing process
26. detection_of_IoT_botnet_attacks_N_BaIoT: This dataset addresses the lack of public botnet datasets, especially for the IoT. It suggests *real* traffic data, gathered from 9 commercial IoT devices authentically infected by Mirai and BASHLITE.
27. Condition monitoring of hydraulic systems: The data set addresses the condition assessment of a hydraulic test rig based on multi sensor data. Four fault types are superimposed with several severity grades impeding selective quantification.
28. Reuter_50_50: The dataset is used for authorship identification in online Writeprint which is a new research field of pattern recognition.
29. Gas Sensor Array Drift Dataset: This archive contains 13910 measurements from 16 chemical sensors utilized in simulations for drift compensation in a discrimination task of 6 gases at various levels of concentrations.
30. OPPORTUNITY Activity Recognition: The OPPORTUNITY Dataset for Human Activity Recognition from Wearable, Object, and Ambient Sensors is a dataset devised to benchmark human activity recognition algorithms (classification, automatic data segmentation, sensor fusion, feature extraction, etc).
31. Nomao: Nomao collects data about places (name, phone, localization...) from many sources.
Deduplication consists in detecting what data refer to the same place.
Instances in the dataset compare 2 spots.
32. MEx: The MEx Multi-modal Exercise dataset contains data of 7 different
physiotherapy exercises, performed by 30 subjects recorded with 2 accelerometers,
a pressure mat and a depth camera.