This dataset includes the data used in our two research papers. GNN4TJ and GNN4IP. 

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The dataset is collected for the purpose of investigating how brainwave signals can be used to industrial insider threat detection. The dataset was connected using Emotiv Insight 5 channels device. The dataset contains data from 17 subjects who accepted to participate in this data collection.

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Design and fabrication outsourcing has made integrated circuits vulnerable to malicious modifications by third parties known as hardware Trojan (HT). Over the last decade, the use of side-channel measurements for detecting the malicious manipulation of the chip has been extensively studied. However, the suggested approaches mostly suffer from two major limitations: reliance on trusted identical chip (e.i. golden chip); untraceable footprints of subtle hardware Trojans which remain inactive during the testing phase.

Instructions: 

See the attached document.

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

 

Instructions: 

The files describe a RNS-based RSA decription processor.

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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.

Instructions: 

Download Zip file and extract it.

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140 Views

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. 

Instructions: 

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.

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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.                                                                                  

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213 Views

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.

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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. 

Instructions: 

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.  

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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 

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Acknowledgment

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).

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2221 Views

This is for BGP anomaly analysis

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24 Views

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