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IEEE Signal Processing Cup 2018 Database - Forensic Camera Model Identification


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.

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Citation Author(s):
Submitted by:
Matthew Stamm
Last updated:
Sat, 06/16/2018 - 23:18
DOI:
10.21227/H2XM2P
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[1] , "IEEE Signal Processing Cup 2018 Database - Forensic Camera Model Identification", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H2XM2P. Accessed: Jun. 22, 2018.
@data{h2xm2p-18,
doi = {10.21227/H2XM2P},
url = {http://dx.doi.org/10.21227/H2XM2P},
author = { },
publisher = {IEEE Dataport},
title = {IEEE Signal Processing Cup 2018 Database - Forensic Camera Model Identification},
year = {2018} }
TY - DATA
T1 - IEEE Signal Processing Cup 2018 Database - Forensic Camera Model Identification
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H2XM2P
ER -
. (2018). IEEE Signal Processing Cup 2018 Database - Forensic Camera Model Identification. IEEE Dataport. http://dx.doi.org/10.21227/H2XM2P
, 2018. IEEE Signal Processing Cup 2018 Database - Forensic Camera Model Identification. Available at: http://dx.doi.org/10.21227/H2XM2P.
. (2018). "IEEE Signal Processing Cup 2018 Database - Forensic Camera Model Identification." Web.
1. . IEEE Signal Processing Cup 2018 Database - Forensic Camera Model Identification [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H2XM2P
. "IEEE Signal Processing Cup 2018 Database - Forensic Camera Model Identification." doi: 10.21227/H2XM2P

Benchmark suite for "An Integrated Approach for Effective Injection Vulnerability Analysis of Web Applications through Security Slicing and Hybrid Constraint Solving"


This dataset is composed of the following benchmarks:

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Dataset Details

Citation Author(s):
Submitted by:
Julian Thome
Last updated:
Fri, 06/08/2018 - 04:12
DOI:
10.21227/H2ZQ1N
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[1] , "Benchmark suite for "An Integrated Approach for Effective Injection Vulnerability Analysis of Web Applications through Security Slicing and Hybrid Constraint Solving"", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H2ZQ1N. Accessed: Jun. 22, 2018.
@data{h2zq1n-18,
doi = {10.21227/H2ZQ1N},
url = {http://dx.doi.org/10.21227/H2ZQ1N},
author = { },
publisher = {IEEE Dataport},
title = {Benchmark suite for "An Integrated Approach for Effective Injection Vulnerability Analysis of Web Applications through Security Slicing and Hybrid Constraint Solving"},
year = {2018} }
TY - DATA
T1 - Benchmark suite for "An Integrated Approach for Effective Injection Vulnerability Analysis of Web Applications through Security Slicing and Hybrid Constraint Solving"
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H2ZQ1N
ER -
. (2018). Benchmark suite for "An Integrated Approach for Effective Injection Vulnerability Analysis of Web Applications through Security Slicing and Hybrid Constraint Solving". IEEE Dataport. http://dx.doi.org/10.21227/H2ZQ1N
, 2018. Benchmark suite for "An Integrated Approach for Effective Injection Vulnerability Analysis of Web Applications through Security Slicing and Hybrid Constraint Solving". Available at: http://dx.doi.org/10.21227/H2ZQ1N.
. (2018). "Benchmark suite for "An Integrated Approach for Effective Injection Vulnerability Analysis of Web Applications through Security Slicing and Hybrid Constraint Solving"." Web.
1. . Benchmark suite for "An Integrated Approach for Effective Injection Vulnerability Analysis of Web Applications through Security Slicing and Hybrid Constraint Solving" [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H2ZQ1N
. "Benchmark suite for "An Integrated Approach for Effective Injection Vulnerability Analysis of Web Applications through Security Slicing and Hybrid Constraint Solving"." doi: 10.21227/H2ZQ1N

Received Signal Strength Based Gait Authentication


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.

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Dataset Details

Citation Author(s):
Submitted by:
Marshed Mohamed
Last updated:
Wed, 05/23/2018 - 11:14
DOI:
10.21227/H2QD5W
 
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[1] , "Received Signal Strength Based Gait Authentication", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H2QD5W. Accessed: Jun. 22, 2018.
@data{h2qd5w-18,
doi = {10.21227/H2QD5W},
url = {http://dx.doi.org/10.21227/H2QD5W},
author = { },
publisher = {IEEE Dataport},
title = {Received Signal Strength Based Gait Authentication},
year = {2018} }
TY - DATA
T1 - Received Signal Strength Based Gait Authentication
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H2QD5W
ER -
. (2018). Received Signal Strength Based Gait Authentication. IEEE Dataport. http://dx.doi.org/10.21227/H2QD5W
, 2018. Received Signal Strength Based Gait Authentication. Available at: http://dx.doi.org/10.21227/H2QD5W.
. (2018). "Received Signal Strength Based Gait Authentication." Web.
1. . Received Signal Strength Based Gait Authentication [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H2QD5W
. "Received Signal Strength Based Gait Authentication." doi: 10.21227/H2QD5W

Debugging Static Analysis


 

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.

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Dataset Details

Citation Author(s):
Submitted by:
Lisa Nguyen Quang Do
Last updated:
Fri, 05/18/2018 - 06:48
DOI:
10.21227/H20W9Q
Data Format:
 
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[1] , "Debugging Static Analysis", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H20W9Q. Accessed: Jun. 22, 2018.
@data{h20w9q-18,
doi = {10.21227/H20W9Q},
url = {http://dx.doi.org/10.21227/H20W9Q},
author = { },
publisher = {IEEE Dataport},
title = {Debugging Static Analysis},
year = {2018} }
TY - DATA
T1 - Debugging Static Analysis
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H20W9Q
ER -
. (2018). Debugging Static Analysis. IEEE Dataport. http://dx.doi.org/10.21227/H20W9Q
, 2018. Debugging Static Analysis. Available at: http://dx.doi.org/10.21227/H20W9Q.
. (2018). "Debugging Static Analysis." Web.
1. . Debugging Static Analysis [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H20W9Q
. "Debugging Static Analysis." doi: 10.21227/H20W9Q

QRNG Machine Learning


Dataset used in paper "Machine Learning Cryptanalysis of a Quantum Random Number Generator" submitted to IEEE TIFS.

 

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No Data files have been uploaded.

Dataset Details

Citation Author(s):
Submitted by:
Nhan Truong
Last updated:
Tue, 05/08/2018 - 03:51
DOI:
10.21227/H2108P
Data Format:
 
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[1] , "QRNG Machine Learning", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H2108P. Accessed: Jun. 22, 2018.
@data{h2108p-18,
doi = {10.21227/H2108P},
url = {http://dx.doi.org/10.21227/H2108P},
author = { },
publisher = {IEEE Dataport},
title = {QRNG Machine Learning},
year = {2018} }
TY - DATA
T1 - QRNG Machine Learning
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H2108P
ER -
. (2018). QRNG Machine Learning. IEEE Dataport. http://dx.doi.org/10.21227/H2108P
, 2018. QRNG Machine Learning. Available at: http://dx.doi.org/10.21227/H2108P.
. (2018). "QRNG Machine Learning." Web.
1. . QRNG Machine Learning [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H2108P
. "QRNG Machine Learning." doi: 10.21227/H2108P

Integrity Coded Databases - Protecting Data Integrity for Outsourced Databases


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

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Dataset Details

Citation Author(s):
Submitted by:
Jyh-haw Yeh
Last updated:
Fri, 04/27/2018 - 15:44
DOI:
10.21227/H21660
Data Format:
 
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[1] , "Integrity Coded Databases - Protecting Data Integrity for Outsourced Databases", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H21660. Accessed: Jun. 22, 2018.
@data{h21660-18,
doi = {10.21227/H21660},
url = {http://dx.doi.org/10.21227/H21660},
author = { },
publisher = {IEEE Dataport},
title = {Integrity Coded Databases - Protecting Data Integrity for Outsourced Databases},
year = {2018} }
TY - DATA
T1 - Integrity Coded Databases - Protecting Data Integrity for Outsourced Databases
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H21660
ER -
. (2018). Integrity Coded Databases - Protecting Data Integrity for Outsourced Databases. IEEE Dataport. http://dx.doi.org/10.21227/H21660
, 2018. Integrity Coded Databases - Protecting Data Integrity for Outsourced Databases. Available at: http://dx.doi.org/10.21227/H21660.
. (2018). "Integrity Coded Databases - Protecting Data Integrity for Outsourced Databases." Web.
1. . Integrity Coded Databases - Protecting Data Integrity for Outsourced Databases [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H21660
. "Integrity Coded Databases - Protecting Data Integrity for Outsourced Databases." doi: 10.21227/H21660

SUT-Lips-DB - A database of lips traces


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.

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OPEN ACCESS Dataset Details

Citation Author(s):
Submitted by:
Dariusz Mrozek
Last updated:
Sat, 06/16/2018 - 23:18
DOI:
10.21227/H2R04P
Data Format:
Links:
 
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[1] , "SUT-Lips-DB - A database of lips traces", IEEE Dataport, 2017. [Online]. Available: http://dx.doi.org/10.21227/H2R04P. Accessed: Jun. 22, 2018.
@data{h2r04p-17,
doi = {10.21227/H2R04P},
url = {http://dx.doi.org/10.21227/H2R04P},
author = { },
publisher = {IEEE Dataport},
title = {SUT-Lips-DB - A database of lips traces},
year = {2017} }
TY - DATA
T1 - SUT-Lips-DB - A database of lips traces
AU -
PY - 2017
PB - IEEE Dataport
UR - 10.21227/H2R04P
ER -
. (2017). SUT-Lips-DB - A database of lips traces. IEEE Dataport. http://dx.doi.org/10.21227/H2R04P
, 2017. SUT-Lips-DB - A database of lips traces. Available at: http://dx.doi.org/10.21227/H2R04P.
. (2017). "SUT-Lips-DB - A database of lips traces." Web.
1. . SUT-Lips-DB - A database of lips traces [Internet]. IEEE Dataport; 2017. Available from : http://dx.doi.org/10.21227/H2R04P
. "SUT-Lips-DB - A database of lips traces." doi: 10.21227/H2R04P

Costas arrays and enumeration to order 1030


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.

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OPEN ACCESS Dataset Details

Citation Author(s):
Submitted by:
James Beard
Last updated:
Sat, 06/16/2018 - 23:18
DOI:
10.21227/H21P42
Data Format:
Links:
 
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[1] , "Costas arrays and enumeration to order 1030", IEEE Dataport, 2017. [Online]. Available: http://dx.doi.org/10.21227/H21P42. Accessed: Jun. 22, 2018.
@data{h21p42-17,
doi = {10.21227/H21P42},
url = {http://dx.doi.org/10.21227/H21P42},
author = { },
publisher = {IEEE Dataport},
title = {Costas arrays and enumeration to order 1030},
year = {2017} }
TY - DATA
T1 - Costas arrays and enumeration to order 1030
AU -
PY - 2017
PB - IEEE Dataport
UR - 10.21227/H21P42
ER -
. (2017). Costas arrays and enumeration to order 1030. IEEE Dataport. http://dx.doi.org/10.21227/H21P42
, 2017. Costas arrays and enumeration to order 1030. Available at: http://dx.doi.org/10.21227/H21P42.
. (2017). "Costas arrays and enumeration to order 1030." Web.
1. . Costas arrays and enumeration to order 1030 [Internet]. IEEE Dataport; 2017. Available from : http://dx.doi.org/10.21227/H21P42
. "Costas arrays and enumeration to order 1030." doi: 10.21227/H21P42

Walsh Spectrum Analysis on Sampling Distributions


The dataset stores a random sampling distribution with cardinality of support of 4,294,967,296 (i.e., two raised to the power of thirty-two). Specifically, the source generator is fixed as a symmetric-key cryptographic function with 64-bit input and 32-bit output. A total of 17,179,869,184 (i.e., two raised to the power of thirty-four) randomly chosen inputs are used to produce the sampling distribution as the dataset. The integer-valued sampling distribution is formatted as 4,294,967,296 (i.e., two raised to the power of thirty-two) entries, and each entry occupies one byte in storage.

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Citation Author(s):
Submitted by:
Yi LU
Last updated:
Sat, 06/16/2018 - 23:18
DOI:
10.21227/H2RC7M
Data Format:
 
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[1] , "Walsh Spectrum Analysis on Sampling Distributions", IEEE Dataport, 2017. [Online]. Available: http://dx.doi.org/10.21227/H2RC7M. Accessed: Jun. 22, 2018.
@data{h2rc7m-17,
doi = {10.21227/H2RC7M},
url = {http://dx.doi.org/10.21227/H2RC7M},
author = { },
publisher = {IEEE Dataport},
title = {Walsh Spectrum Analysis on Sampling Distributions},
year = {2017} }
TY - DATA
T1 - Walsh Spectrum Analysis on Sampling Distributions
AU -
PY - 2017
PB - IEEE Dataport
UR - 10.21227/H2RC7M
ER -
. (2017). Walsh Spectrum Analysis on Sampling Distributions. IEEE Dataport. http://dx.doi.org/10.21227/H2RC7M
, 2017. Walsh Spectrum Analysis on Sampling Distributions. Available at: http://dx.doi.org/10.21227/H2RC7M.
. (2017). "Walsh Spectrum Analysis on Sampling Distributions." Web.
1. . Walsh Spectrum Analysis on Sampling Distributions [Internet]. IEEE Dataport; 2017. Available from : http://dx.doi.org/10.21227/H2RC7M
. "Walsh Spectrum Analysis on Sampling Distributions." doi: 10.21227/H2RC7M

Dataset malware/beningn permissions Android


This dataset is a result of my research production into machine learning in android security. The data was obtained by a process that consisted to map a binary vector of permissions used for each application analyzed {1=used, 0=no used}. Moreover, the samples of malware/benign were devided by "Type"; 1 malware and 0 non-malware.

When I did my research, the datasets of malware and benign Android applications were not available, then I give to the community a part of my research results for the future works.

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Dataset Details

Citation Author(s):
Submitted by:
Christian Urcuqui
Last updated:
Thu, 11/17/2016 - 10:43
DOI:
10.21227/H26P4M
Data Format:
Links:
 
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[1] , "Dataset malware/beningn permissions Android", IEEE Dataport, 2016. [Online]. Available: http://dx.doi.org/10.21227/H26P4M. Accessed: Jun. 22, 2018.
@data{h26p4m-16,
doi = {10.21227/H26P4M},
url = {http://dx.doi.org/10.21227/H26P4M},
author = { },
publisher = {IEEE Dataport},
title = {Dataset malware/beningn permissions Android},
year = {2016} }
TY - DATA
T1 - Dataset malware/beningn permissions Android
AU -
PY - 2016
PB - IEEE Dataport
UR - 10.21227/H26P4M
ER -
. (2016). Dataset malware/beningn permissions Android. IEEE Dataport. http://dx.doi.org/10.21227/H26P4M
, 2016. Dataset malware/beningn permissions Android. Available at: http://dx.doi.org/10.21227/H26P4M.
. (2016). "Dataset malware/beningn permissions Android." Web.
1. . Dataset malware/beningn permissions Android [Internet]. IEEE Dataport; 2016. Available from : http://dx.doi.org/10.21227/H26P4M
. "Dataset malware/beningn permissions Android." doi: 10.21227/H26P4M