This dataset covers cellular communication signals in the SCF format. There is a total of 60000 signal instances, 36000 of them are reserved as training data and the rest is for the test. The SNR levels are between 1 dB and 15 dB.

Instructions: 

For each SNR level, the training dataset has four files. The user can concatenate these files. The same procedure is valid for the test dataset.

 

The labels mean:

 

0 -> AWGN (no signal in the spectrum)

 

1 -> UMTS

 

2 -> LTE

 

3 -> GSM

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2449 Views

The 2020 Data Fusion Contest, organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (GRSS) and the Technical University of Munich, aims to promote research in large-scale land cover mapping based on weakly supervised learning from globally available multimodal satellite data. The task is to train a machine learning model for global land cover mapping based on weakly annotated samples.

Last Updated On: 
Mon, 01/25/2021 - 09:03

Dataset for Telugu Handwritten Gunintam

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436 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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4337 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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1716 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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773 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.

Categories:
1919 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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1888 Views

This dataset includes the measurements of a simulated vehicle inside a Gazebo simulation using different sensors: a simulated UWB tag, a IMU and a PX4Flow. 

Instructions: 

Please, if you use the datase, put a reference to our article: 

 

Barral, V., Suárez-Casal, P., Escudero, C. J., & García-Naya, J. A. (2019). Multi-Sensor Accurate Forklift Location and Tracking Simulation in Industrial Indoor Environments. Electronics8(10), 1152. https://doi.org/10.3390/electronics8101152

 

The dataset includes several .mat files for each scenario. Each file contains a ROS set of topics. To read each topic is needed the Matlab Robotics System Toolbox. An example script is added.

 

AllMeasurementsScenarioXX.mat contains the sensor data and position estimates on scenario XX.

The available topics are:

- /gtec/gazebo/pos  : Ground truth positions

- /gtec/kfpos_uwb : location estimates, only UWB

- /gtec/kfpos_uwb_imu : location estimates, UWB + IMU

- /gtec/kfpos_uwb_imu_px4: location estimates, UWB + IMU + PX4 FLOW

- /gtec/gazebo/imu : IMU data

- /gtec/gazebo/uwb/ranging/0: UWB ranging data

- /gtec/gazebo/px4flow: PX4Flow

 

AnchorsScenarioXX.mat contains the positions of the UWB anchors in scenario XX.

The available topics are:

 

/gtec/gazebo/uwb/anchors/0 : The positions of each anchor.

 

ScenarioXXWalls.mat includes the position of the walls in the scenario XX.

The available topics are:

 

/tf : The positions of each model in the scenario.

 

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716 Views

This dataset is part of our research on malware detection and classification using Deep Learning. It contains 42,797 malware API call sequences and 1,079 goodware API call sequences. Each API call sequence is composed of the first 100 non-repeated consecutive API calls associated with the parent process, extracted from the 'calls' elements of Cuckoo Sandbox reports.

Instructions: 

* FEATURES *

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

Column name: t_0 ... t_99
Description: API call
Type: Integer (0-306)

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

API Calls: ['NtOpenThread', 'ExitWindowsEx', 'FindResourceW', 'CryptExportKey', 'CreateRemoteThreadEx', 'MessageBoxTimeoutW', 'InternetCrackUrlW', 'StartServiceW', 'GetFileSize', 'GetVolumeNameForVolumeMountPointW', 'GetFileInformationByHandle', 'CryptAcquireContextW', 'RtlDecompressBuffer', 'SetWindowsHookExA', 'RegSetValueExW', 'LookupAccountSidW', 'SetUnhandledExceptionFilter', 'InternetConnectA', 'GetComputerNameW', 'RegEnumValueA', 'NtOpenFile', 'NtSaveKeyEx', 'HttpOpenRequestA', 'recv', 'GetFileSizeEx', 'LoadStringW', 'SetInformationJobObject', 'WSAConnect', 'CryptDecrypt', 'GetTimeZoneInformation', 'InternetOpenW', 'CoInitializeEx', 'CryptGenKey', 'GetAsyncKeyState', 'NtQueryInformationFile', 'GetSystemMetrics', 'NtDeleteValueKey', 'NtOpenKeyEx', 'sendto', 'IsDebuggerPresent', 'RegQueryInfoKeyW', 'NetShareEnum', 'InternetOpenUrlW', 'WSASocketA', 'CopyFileExW', 'connect', 'ShellExecuteExW', 'SearchPathW', 'GetUserNameA', 'InternetOpenUrlA', 'LdrUnloadDll', 'EnumServicesStatusW', 'EnumServicesStatusA', 'WSASend', 'CopyFileW', 'NtDeleteFile', 'CreateActCtxW', 'timeGetTime', 'MessageBoxTimeoutA', 'CreateServiceA', 'FindResourceExW', 'WSAAccept', 'InternetConnectW', 'HttpSendRequestA', 'GetVolumePathNameW', 'RegCloseKey', 'InternetGetConnectedStateExW', 'GetAdaptersInfo', 'shutdown', 'NtQueryMultipleValueKey', 'NtQueryKey', 'GetSystemWindowsDirectoryW', 'GlobalMemoryStatusEx', 'GetFileAttributesExW', 'OpenServiceW', 'getsockname', 'LoadStringA', 'UnhookWindowsHookEx', 'NtCreateUserProcess', 'Process32NextW', 'CreateThread', 'LoadResource', 'GetSystemTimeAsFileTime', 'SetStdHandle', 'CoCreateInstanceEx', 'GetSystemDirectoryA', 'NtCreateMutant', 'RegCreateKeyExW', 'IWbemServices_ExecQuery', 'NtDuplicateObject', 'Thread32First', 'OpenSCManagerW', 'CreateServiceW', 'GetFileType', 'MoveFileWithProgressW', 'NtDeviceIoControlFile', 'GetFileInformationByHandleEx', 'CopyFileA', 'NtLoadKey', 'GetNativeSystemInfo', 'NtOpenProcess', 'CryptUnprotectMemory', 'InternetWriteFile', 'ReadProcessMemory', 'gethostbyname', 'WSASendTo', 'NtOpenSection', 'listen', 'WSAStartup', 'socket', 'OleInitialize', 'FindResourceA', 'RegOpenKeyExA', 'RegEnumKeyExA', 'NtQueryDirectoryFile', 'CertOpenSystemStoreW', 'ControlService', 'LdrGetProcedureAddress', 'GlobalMemoryStatus', 'NtSetInformationFile', 'OutputDebugStringA', 'GetAdaptersAddresses', 'CoInitializeSecurity', 'RegQueryValueExA', 'NtQueryFullAttributesFile', 'DeviceIoControl', '__anomaly__', 'DeleteFileW', 'GetShortPathNameW', 'NtGetContextThread', 'GetKeyboardState', 'RemoveDirectoryA', 'InternetSetStatusCallback', 'NtResumeThread', 'SetFileInformationByHandle', 'NtCreateSection', 'NtQueueApcThread', 'accept', 'DecryptMessage', 'GetUserNameExW', 'SizeofResource', 'RegQueryValueExW', 'SetWindowsHookExW', 'HttpOpenRequestW', 'CreateDirectoryW', 'InternetOpenA', 'GetFileVersionInfoExW', 'FindWindowA', 'closesocket', 'RtlAddVectoredExceptionHandler', 'IWbemServices_ExecMethod', 'GetDiskFreeSpaceExW', 'TaskDialog', 'WriteConsoleW', 'CryptEncrypt', 'WSARecvFrom', 'NtOpenMutant', 'CoGetClassObject', 'NtQueryValueKey', 'NtDelayExecution', 'select', 'HttpQueryInfoA', 'GetVolumePathNamesForVolumeNameW', 'RegDeleteValueW', 'InternetCrackUrlA', 'OpenServiceA', 'InternetSetOptionA', 'CreateDirectoryExW', 'bind', 'NtShutdownSystem', 'DeleteUrlCacheEntryA', 'NtMapViewOfSection', 'LdrGetDllHandle', 'NtCreateKey', 'GetKeyState', 'CreateRemoteThread', 'NtEnumerateValueKey', 'SetFileAttributesW', 'NtUnmapViewOfSection', 'RegDeleteValueA', 'CreateJobObjectW', 'send', 'NtDeleteKey', 'SetEndOfFile', 'GetUserNameExA', 'GetComputerNameA', 'URLDownloadToFileW', 'NtFreeVirtualMemory', 'recvfrom', 'NtUnloadDriver', 'NtTerminateThread', 'CryptUnprotectData', 'NtCreateThreadEx', 'DeleteService', 'GetFileAttributesW', 'GetFileVersionInfoSizeExW', 'OpenSCManagerA', 'WriteProcessMemory', 'GetSystemInfo', 'SetFilePointer', 'Module32FirstW', 'ioctlsocket', 'RegEnumKeyW', 'RtlCompressBuffer', 'SendNotifyMessageW', 'GetAddrInfoW', 'CryptProtectData', 'Thread32Next', 'NtAllocateVirtualMemory', 'RegEnumKeyExW', 'RegSetValueExA', 'DrawTextExA', 'CreateToolhelp32Snapshot', 'FindWindowW', 'CoUninitialize', 'NtClose', 'WSARecv', 'CertOpenStore', 'InternetGetConnectedState', 'RtlAddVectoredContinueHandler', 'RegDeleteKeyW', 'SHGetSpecialFolderLocation', 'CreateProcessInternalW', 'NtCreateDirectoryObject', 'EnumWindows', 'DrawTextExW', 'RegEnumValueW', 'SendNotifyMessageA', 'NtProtectVirtualMemory', 'NetUserGetLocalGroups', 'GetUserNameW', 'WSASocketW', 'getaddrinfo', 'AssignProcessToJobObject', 'SetFileTime', 'WriteConsoleA', 'CryptDecodeObjectEx', 'EncryptMessage', 'system', 'NtSetContextThread', 'LdrLoadDll', 'InternetGetConnectedStateExA', 'RtlCreateUserThread', 'GetCursorPos', 'Module32NextW', 'RegCreateKeyExA', 'NtLoadDriver', 'NetUserGetInfo', 'SHGetFolderPathW', 'GetBestInterfaceEx', 'CertControlStore', 'StartServiceA', 'NtWriteFile', 'Process32FirstW', 'NtReadVirtualMemory', 'GetDiskFreeSpaceW', 'GetFileVersionInfoW', 'FindFirstFileExW', 'FindWindowExW', 'GetSystemWindowsDirectoryA', 'RegOpenKeyExW', 'CoCreateInstance', 'NtQuerySystemInformation', 'LookupPrivilegeValueW', 'NtReadFile', 'ReadCabinetState', 'GetForegroundWindow', 'InternetCloseHandle', 'FindWindowExA', 'ObtainUserAgentString', 'CryptCreateHash', 'GetTempPathW', 'CryptProtectMemory', 'NetGetJoinInformation', 'NtOpenKey', 'GetSystemDirectoryW', 'DnsQuery_A', 'RegQueryInfoKeyA', 'NtEnumerateKey', 'RegisterHotKey', 'RemoveDirectoryW', 'FindFirstFileExA', 'CertOpenSystemStoreA', 'NtTerminateProcess', 'NtSetValueKey', 'CryptAcquireContextA', 'SetErrorMode', 'UuidCreate', 'RtlRemoveVectoredExceptionHandler', 'RegDeleteKeyA', 'setsockopt', 'FindResourceExA', 'NtSuspendThread', 'GetFileVersionInfoSizeW', 'NtOpenDirectoryObject', 'InternetQueryOptionA', 'InternetReadFile', 'NtCreateFile', 'NtQueryAttributesFile', 'HttpSendRequestW', 'CryptHashMessage', 'CryptHashData', 'NtWriteVirtualMemory', 'SetFilePointerEx', 'CertCreateCertificateContext', 'DeleteUrlCacheEntryW', '__exception__']

* 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 *

"Oliveira, Angelo; Sassi, Renato José (2019): Behavioral Malware Detection Using Deep Graph Convolutional Neural Networks. TechRxiv. Preprint." at https://doi.org/10.36227/techrxiv.10043099.v1 Please feel free to contact me for any further information.

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4072 Views

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