The data is the Xilinx ISE project and related HDL files.



The files describe a RNS-based RSA decription processor.


Biometric-based hand modality is considered as one of the most popular biometric technologies especially in forensic applications. Hand recognition is an active research topic in recent years and in order to promote hand’s recognition research, the REGIM-Lab.: REsearch Groups in Intelligent Machines, ENIS, University of Sfax, Tunisia provides the REgim Sfax Tunisian hand database (REST database) freely of charge to mainly hand and palmprint recognition researchers.


Download Zip file and extract it.


The provided dataset is obtained by crawling through various websites to identify all the possible webpages that which can be used to determine to what degree they are exposed to attacks. 


The dataset contains only two columns namely:

1-Link :- containing the crawled URLs (Uniform Resource Locator) for different websites.

2-Priority:- which labels each URL with one of three labels.


Data set of 26/11 Mumbai attack is based on Mumbai Terrorist Attacks 2008 India Ministry of External Affairs Dossier and News reports. 10 terrorist operated in India distributed in five sub-groups, simultaneously 3 other person comes in light as per report those were having in continue touch with these terrorist from Pakistan and giving them instructions.                                                                                  


A dataset of LDoS attacks against bottleneck links in software-defined networks. LDoS attacks are based on a flawed implementation of the TCP congestion control mechanism and can seriously affect legitimate traffic on bottlenecked or shared links in software-defined networks.


This dataset contains RF signals from drone remote controllers (RCs) of different makes and models. The RF signals transmitted by the drone RCs to communicate with the drones are intercepted and recorded by a passive RF surveillance system, which consists of a high-frequency oscilloscope, directional grid antenna, and low-noise power amplifier. The drones were idle during the data capture process. All the drone RCs transmit signals in the 2.4 GHz band. There are 17 drone RCs from eight different manufacturers and ~1000 RF signals per drone RC, each spanning a duration of 0.25 ms. 


The dataset contains ~1000 RF signals in .mat format from the remote controllers (RCs) of the following drones:

  • DJI (5): Inspire 1 Pro, Matrice 100, Matrice 600*, Phantom 4 Pro*, Phantom 3 
  • Spektrum (4): DX5e, DX6e, DX6i, JR X9303
  • Futaba (1): T8FG
  • Graupner (1): MC32
  • HobbyKing (1): HK-T6A
  • FlySky (1): FS-T6
  • Turnigy (1): 9X
  • Jeti Duplex (1): DC-16.

In the dataset, there are two pairs of RCs for the drones indicated by an asterisk above, making a total of 17 drone RCs. Each RF signal contains 5 million samples and spans a time period of 0.25 ms. 

The scripts provided with the dataset defines a class to create drone RC objects and creates a database of objects as well as a database in table format with all the available information, such as make, model, raw RF signal, sampling frequency, etc. The scripts also include functions to visualize data and extract a few example features from the raw RF signal (e.g., transient signal start point). Instructions for using the scripts are included at the top of each script and can also be viewed by typing help scriptName in MATLAB command window.  

The drone RC RF dataset was used in the following papers:

  • M. Ezuma, F. Erden, C. Kumar, O. Ozdemir, and I. Guvenc, "Micro-UAV detection and classification from RF fingerprints using machine learning techniques," in Proc. IEEE Aerosp. Conf., Big Sky, MT, Mar. 2019, pp. 1-13.
  • M. Ezuma, F. Erden, C. K. Anjinappa, O. Ozdemir, and I. Guvenc, "Detection and classification of UAVs using RF fingerprints in the presence of Wi-Fi and Bluetooth interference," IEEE Open J. Commun. Soc., vol. 1, no. 1, pp. 60-79, Nov. 2019.
  • E. Ozturk, F. Erden, and I. Guvenc, "RF-based low-SNR classification of UAVs using convolutional neural networks." arXiv preprint arXiv:2009.05519, Sept. 2020.

Other details regarding the dataset and data collection and processing can be found in the above papers and attached documentation.  


Author Contributions:

  • Experiment design: O. Ozdemir and M. Ezuma
  • Data collection:  M. Ezuma
  • Scripts: F. Erden and C. K. Anjinappa
  • Documentation: F. Erden
  • Supervision, revision, and funding: I. Guvenc 



This work was supported in part by NASA through the Federal Award under Grant NNX17AJ94A, and in part by NSF under CNS-1939334 (AERPAW, one of NSF's Platforms for Advanced Wireless Research (PAWR) projects).


This is for BGP anomaly analysis


Gaming consoles are very common connected devices which have evolved in functionality and applications (games and beyond) they support. This diversity of traffic generated from these consoles has diverse quality of service (QoS) requirements. However, in order to offer diverse QoS, ISPs and operators must be able to classify this traffic. To enable research in traffic classification (Machine Learning based or other), we have generated and collected this dataset. This is a labelled dataset collected from a gaming console, PlayStation 4.


Download Microsoft Network Monitor (at the following link: to be able to access the data. Open the capture file and then wait for all the collected frames to be loaded. The data set was collected using Microsoft Network Monitor 3.4. The traffic is Labelled by number, time and day, Source and Destination IP, Protocol, length and description. Using Microsoft Network Monitor, there is a way to Filter by Media type (check the following link: To navigate the data easily, you can apply a filter on the media type by putting it Ethernet meaning that only the data exchanged between the Laptop and the PlayStation will show. The Excel sheet included with the dataset contains the date and the time of each capture and also when each activity was running and when it was stopped making it easy to identify the data. Refer to the time delay report attached for more information about the time synchronization aspects between the data capture and the PlayStation.


The data are four Xilinx ISE projects for Montgomery modualr multiplication and modular exponentiation.


There are 4 directions in the data, the first 2 of which are Montgomery modular multiplications, and the last 2 of which are modular exponentiations.


The CHU Surveillance Violence Dataset (CSVD) is a collection of CCTV footage of violent and non-violent actions aiming to characterize the composition of violent actions into more specific actions. We produced several simple action classes for violent and non-violent actions do add variety and better distribution among simple and complex action classes for RGB and Action Silhouette Videos (enhanced Optical Flow Images) with their localized actions.