We propose a general framework for using deep learning to detect vulnerabilities, named SySeVR. For evaluate the SySeVR, we collect the Semantics-based Vulnerability Candidate (SeVC) dataset, which contains all kinds of vulnerabilities that are available from the National Vulnerability Database (NVD) and the Software Assurance Reference Dataset (SARD).



The dataset contains several publicly available Bug Bounty Program policy documents collected from the internet.

These were used to develop the theory of Bug Bounty Program practices.


This database consists of the data used for the 2018 IEEE Signal Processing Cup.  This iteration of the Signal Processing Cup was a forensic camera model identification challenge.  Teams of undergraduate students were tasked with building a system capable of determining type of camera (manufacturer and model) that captured a digital image without relying on metadata.


This dataset consists of both full images and image blocks captured by a variety of different cameras.  These images are available for research purposes.


Expansion of wireless body area networks (WBANs) applications such as health-care, m-banking, and others  has lead to vulnerability of privacy and personal data. An effective and unobtrusive natural method of authentication is therefore a necessity in such applications. Accelerometer-based gait recognition has become an attractive solution, however, continuous sampling of accelerometer data reduces the battery life of wearables. This paper investigates the usage of received signal strength indicator (RSSI) as a source of gait recognition.



Static analysis is increasingly used by companies and individual code developers to detect bugs and security vulnerabilities. As programs grow more complex, the analyses have to support new code concepts, frameworks and libraries. However, static-analysis code itself is also prone to bugs. While more complex analyses are written and used in production systems every day, the cost of debugging and fixing them also increases tremendously.


Dataset used in paper "Machine Learning Cryptanalysis of a Quantum Random Number Generator" published at IEEE TIFS https://ieeexplore.ieee.org/document/8396276.



The two dataset files contains the experimental results for ICDB DMode and ICDB AMode.


A database of lips traces
Cheiloscopy is a forensic investigation technique that deals with identification of humans based on lips traces. Lip prints are unique and permanent for each individual, and next to the fingerprinting, dental identification, and DNA analysis can be one of the basis for criminal/forensics analysis.


SUT-Lips-DB database is free for scientific and testing purposes. However, you are asked to cite the data set and our papers mentioned at Home Project web site every time when you publish your own research conducted with the use of our data set or when you compare your own results with ours.

The main ZIP archive contains several folders. Each folder may contain several lip traces as JPG files only for one person. Data are anonimized. The name of the folder contains the informormation on the gender of the person. Additional CSV file contains information about year of birth of people for who we collected samples.


Costas arrays are permutation matrices that meet the added Costas condition that, when used as a frequency-hop scheme, allow at most one time-and-frequency-offset signal bin to overlap another.  Databases to various orders have been available for many years.  Here we have a database that is far more extensive than any available before it.  A very powerful and easy-to-use Windows utility with a GUI accompanies the database.


Download the file GetStarted.zip.  This file contains the Instructions as a PDF file, the extraction and analysis utility in its own ZIP file, and several information files includign an enumeration database in an Excel file.


Unpack this file in a folder that you want to be the location of your Costas array database.  Be sure and unpack subfolders, so that you dee subfolders /Searches and /Generated when you are done.  Folder /Searches contains all Costas arrays to order 29, and folder /Generated contains all generated Costas arrays to order 100.  The file Read_CA_Database_00.zip contains the extraction and analysis utility.  It may be extracted in-place or, if the database is on a network drive or other location inconvenient for DLLs, in its own folder anywhere on a local drive such as your C:\ drive.  See the Instructions PDF for details.


Then, as you need them, add these files: CA_Database_101-200.zip        More data for /Generated folder CA_Database_201-300.zip        More data for /Generated folder CA_Database_301-400.zip        More data for /Generated folder CA_Database_401-500.zip        More data for /Generated folder CA_Database_501-600.zip        More data for /Generated folder CA_Database_601-700.zip        More data for /Generated folder CA_Database_701-800.zip        More data for /Generated folder CA_Database_801-900.zip        More data for /Generated folder CA_Database_901-950.zip       More data for /Generated folder

CA_Database_951-1000.zip    More data for /Generated folder CA_Database_1001-1030.zip    More data for /Generated folder


This is a file that was produced by the extraction/analysis utility FrHop_LUB_Database.zip        Frequency hop LUB list; useful with PLL-based waveform generators


For further information, see the file Costas Arrays to Order 1030 INSTRUCTIONS.pdf