Summary: The archive of DCLN project (https://sourceforge.net/projects/dcln/) is provided.

Code & Script: Written in C/C++, running Shell scripts on Linux system. Mature package release v2.0 available for download. Check './dcln.sh' for usage info.

Document: Details of hyperparameters tuning, data preprocessing and code compiling are given.

Data: Four nonlinear simulation datasets are provided (Fig. 2 of the main paper). Each study has ~2000 training samples and ~2000 test samples.

Instructions: 

unzip files, check scripts and document, prepare input data, and compile the code.

'archive-2.0.6.zip': Last version 2.0.6, simulation data and document.

'rerun-2.0.6.zip': Rerun results with v2.0.6.

'legacy.zip': Older versions.

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1202 Views

This data contains EMG records of forearm movements. this data can be used for the learning process for students and lecturers or researchers. The sensor used to record data is "Myo Arm-Band". The data is equipped with eight features and ends with the arm movement label still using the Indonesian language term.

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257 Views

The development of technology also influences changes in campaign patterns. Campaign activities are part of the process of Election of Regional Heads. The aim of the campaign is to mobilize public participation, which is carried out directly or through social media. Social media becomes a channel for interaction between candidates and their supporters. Interactions that occur during the campaign period can be one indicator of the success of the closeness between voters and candidates. This study aims to get the pattern of campaign interactions that occur on Twitter social media channels.

Instructions: 

This a csv file. Please use approriate applicationThis file containing table of twitter interaction about Regional Head Election on Central Java, IndonesiaThe analysis and paper work on http://ejnteti.jteti.ugm.ac.id/index.php/JNTETI/article/view/499/417

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109 Views

This data consists of ten gestures. The format is CSV, arranged in thirty features that end with a label. Each movement is repeated five times and the coordinates are obtained. The sensor used is LeapMotion. This data can be used as a means of machine learning exercises. can be used for students learning machine learning subjects. articles that have used this data can be seen at the link: https://doi.org/10.1109/CENIM.2018.8711397

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676 Views

This dataset is part of my PhD research on malware detection and classification using Deep Learning. It contains static analysis data: Top-1000 imported functions extracted from the 'pe_imports' elements of Cuckoo Sandbox reports. PE malware examples were downloaded from virusshare.com. PE goodware examples were downloaded from portableapps.com and from Windows 7 x86 directories.

Instructions: 

* FEATURES *

Column name: hash
Description: MD5 hash of the example
Type: 32 bytes string

Column name: GetProcAddress
Description: Most imported function (1st)
Type: 0 (Not imported) or 1 (Imported)

...

Column name: LookupAccountSidW
Description: Least imported function (1000th)
Type: 0 (Not imported) or 1 (Imported)

Column name: malware
Description: Class
Type: 0 (Goodware) or 1 (Malware)

* ACKNOWLEDGMENTS *

We would like to thank: Cuckoo Sandbox for developing such an amazing dynamic analysis environment!
VirusShare! Because sharing is caring!
Universidade Nove de Julho for supporting this research.
Coordination for the Improvement of Higher Education Personnel (CAPES) for supporting this research.

* CITATIONS *

Please refer to the dataset DOI.
Please feel free to contact me for any further information.

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2829 Views

This dataset is part of my PhD research on malware detection and classification using Deep Learning. It contains static analysis data: Raw PE byte stream rescaled to a 32 x 32 greyscale image using the Nearest Neighbor Interpolation algorithm and then flattened to a 1024 bytes vector. PE malware examples were downloaded from virusshare.com. PE goodware examples were downloaded from portableapps.com and from Windows 7 x86 directories.

Instructions: 

* FEATURES *

Column name: hash
Description: MD5 hash of the example
Type: 32 bytes string

Column name: pix_0
Description: The first greyscale pixel value
Type: Integer (0-255)

Column name: pix_1023
Description: The last greyscale pixel value
Type: Integer (0-255)

Column name: malware
Description: Class
Type: 0 (Goodware) or 1 (Malware)

* ACKNOWLEDGMENTS *

We would like to thank: Cuckoo Sandbox for developing such an amazing dynamic analysis environment!
VirusShare! Because sharing is caring!
Universidade Nove de Julho for supporting this research.
Coordination for the Improvement of Higher Education Personnel (CAPES) for supporting this research.

* CITATIONS *

Please refer to the dataset DOI.
Please feel free to contact me for any further information.

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883 Views

This dataset comes up as a benchmark dataset for machines to automatically recognizing the handwritten assamese digists (numerals) by extracting useful features by analyzing the structure. The Assamese language comprises of a total of 10 digits from 0 to 9. We have collected a total of 516 handwritten digits from 52 native assamese people irrespective of their age (12-86 years), gender, educational background etc. The digits are captured in .jpeg format using a paint mobile application developed by us which automatically saves the images in the internal storage of the mobile.

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445 Views

This dataset is part of my PhD research on malware detection and classification using Deep Learning. It contains static analysis data (PE Section Headers of the .text, .code and CODE sections) extracted from the 'pe_sections' elements of Cuckoo Sandbox reports. PE malware examples were downloaded from virusshare.com. PE goodware examples were downloaded from portableapps.com and from Windows 7 x86 directories.

Instructions: 

* FEATURES *

Column name: hash
Description: MD5 hash of the example
Type: 32 bytes string

Column name: size_of_data
Description: The size of the section on disk
Type: Integer

Column name: virtual_address
Description: Memory address of the first byte of the section relative to the image base
Type: Integer

Column name: entropy
Description: Calculated entropy of the section
Type: Float

Column name: virtual_size
Description: The size of the section when loaded into memory
Type: Integer

Column name: malware
Description: Class
Type: 0 (Goodware) or 1 (Malware)

* ACKNOWLEDGMENTS *

We would like to thank: Cuckoo Sandbox for developing such an amazing dynamic analysis environment!
VirusShare! Because sharing is caring!
Universidade Nove de Julho for supporting this research.
Coordination for the Improvement of Higher Education Personnel (CAPES) for supporting this research.

* CITATIONS *

Please refer to the dataset DOI.
Please feel free to contact me for any further information.

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1200 Views

An accurate and reliable image-based quantification system for blueberries may be useful for the automation of harvest management. It may also serve as the basis for controlling robotic harvesting systems. Quantification of blueberries from images is a challenging task due to occlusions, differences in size, illumination conditions and the irregular amount of blueberries that can be present in an image. This paper proposes the quantification per image and per batch of blueberries in the wild, using high definition images captured using a mobile device.

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1084 Views

ASNM datasets include records consisting of many features, that express various properties and characteristics of TCP communications. These features are called Advanced Security Network Metrics (ASNM) and were designed with the intention to discern legitimate and malicious connections (especially intrusions).

Instructions: 

ASNM datasets were created one by one during our long-term research. The following listing contains references to descriptions of particular datasets with their download locations:

 

  • ASNM-NPBO Dataset - contains non-payload-based obfuscation techniques applied onto malicious and some of legitimate traffic. It was created in 2015.
  • ASNM-TUN Dataset - contains tunneling obfuscation techniques applied to malicious traffic. It was created in 2014.
  • ASNM-CDX-2009 Dataset - contains ASNM features extracted from tcpdumps of CDX 2009 dataset. It misses few newer ASNM features. It was created in 2013.
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2391 Views

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