Saravanan666/pypi-org huggingface dataset:
Package: malnetlib
MalNetLibMalNetLib is a Python library for parsing PE files made with .NET (based ondnlib).InstallationInstall the library (available onpypi.org):$python3-mpipinstallmalnetlibCompilednlib.dll(required for the project) :# Debian$sudoaptinstall-ydotnet-sdk-6.0# Arch$sudopacman-Sdotnet-sdk-6.0

$gitclone--depth=1https://github.com/0xd4d/dnlib
$cddnlib
$dotnetbuild
$cd..
$cp./dnlib/Examples/bin/Debug/net6.0/dnlib.dll.
$rm-rfdnlibExamplesNjRATNjRATis a remote access trojan (RAT).You can usemalnetlibto extract the configuration of NjRAT which is stored inside .NET class attributes. Here is an example with the a sample ofNjRATand the scriptnjrat_extractor.py:pe=DotNetPE(args.sample)ok_class=pe.get_object("OK")njrat_conf={"host":ok_class.get_attribute("HH").get_value(),"port":ok_class.get_attribute("P").get_value(),"install_directory":"%"+ok_class.get_attribute("DR").get_value()+"%","install_name":ok_class.get_attribute("EXE").get_value(),"startup":ok_class.get_attribute("sf").get_value(),"campain_id":b64decode(ok_class.get_attribute("VN").get_value()).decode("UTF-8"),"version":ok_class.get_attribute("VR").get_value(),"network_separator":ok_class.get_attribute("Y").get_value(),"mutex":ok_class.get_attribute("RG").get_value()}print(dumps(njrat_conf)){"host":"2.tcp.eu.ngrok.io","port":"10008","install_directory":"%TEMP%","install_name":"server.exe","startup":"Software\\Microsoft\\Windows\\CurrentVersion\\Run","campain_id":"HacKed","version":"im523","network_separator":"|'|'|","mutex":"25ffb1a66b4748fe7537df7005cc8e55"}vw0rmVJW0rm(aka Vengeance Justice Worm) is a publicly available, modular JavaScript RAT. Other variants include a Visual Basic Script (VBS) based worm titled vw0rm (Vengeance Worm).You can usemalnetlibto list and extract .NET resources from avw0rmdropper.>>>frommalnetlib.modelsimportDotNetPE>>>pe=DotNetPE("30d64daeb3e69ea6dde202a9d519f6ee17614af6e505dd2e5788017c3be4abd8")>>>resources=pe.get_resources()>>>resources[<ResourceWindows_Task_Manager.Resources.resources0x0>,<ResourceWindows_Task_Manager.proccesing.resources0x4806>]>>>resources[0].save("/tmp/output.bin")>>> resources[0].read()
b'[...]On error resume next\r\n\r\nj = array("WScript.Shell","Scripting.FileSystemObject","Shell.Application","Microsoft.XMLHTTP")
\r\ng = array("HKCU","HKLM","HKCU\\vw0rm","\\Software\\Microsoft\\Windows\\CurrentVersion\\Run\\","HKLM\\SOFTWARE\\Classes\\","REG_SZ","\\defaulticon\\")\r\ny=
array("winmgmts:","win32_logicaldisk","Win32_OperatingSystem","winmgmts:\\\\localhost\\root\\securitycenter","AntiVirusProduct")\r\n\r\nfunction go(m)\r\n
Package: malnet-task
malnet-taskmalnet malware analysis task utilsInstallation$gitclonessh://git@git.kongkongss.com/malwarenet/malnet-task.git
$cdmalnet-task# `run as developer`$pipinstallmalnet-task-e.Getting Startedwaitingfor...Running the tests$cdmalnet-task
$pytest-sChangelogrelease ChangelogTODOsAuthorskongkong Jiang-Initial work-jykerLicenseThis project is licensed under the MIT License - see theLICENSEfile for details
Package: maloja
A toolkit for use with a VMware cloud. Runs from the command line in Python 3.4 or above.Stay in charge of your cloudMaloja surveys your VMware virtual infrastructure and saves to a tidy
collection of YAML files.With only a text editor you can build up a design for new cloud assets, pasting
in the YAML descriptions of what you need.The Malojaplancommand will tell you how your design looks and where there
are mistakes.The Malojabuildcommand will make the design for you via the VMware REST API.The Malojainspectcommand will check what’s built against the original design
and warn you of any gaps.You can writepluginsof your own to work with the Maloja framework.Early adopters/developersRead and follow theInstall notes.Build the HTML manual.Follow theQuick Start guide.End users onlyUsepipto install Maloja from a distributed package:pip install maloja-0.8.0.zip
Package: mal-scraper
MyAnimeList web scraper is a Python library for gathering data for analysis.Installation & UsageInstallation is simple:pip install mal-scraperPlease use thedocumentationfor
the reference and usage examples.For your convenience followSemantic Versioning.DevelopmentPlease see theContributingdocumentation page for full details, and especially look at the tips section.After cloning, and creating a virtualenv, install the development dependencies:pip install -e .[develop]To run the all tests, skipping the python interpreters you don’t have:tox --skip-missing-interpretersProject Notes:Tests will always mock requests to the internet. You can set the environment
variableLIVE_RESPONSES=1to properly test web scraping.You can look at coverage results insidehtmlcov/index.html.Note, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxChangelog0.3.0 (2017-05-02)Fix various issues on anime pagesRenameretrieve_animetoget_animefor consistency (backwards-incompatible)0.2.1 (2017-05-01)Add Season as an Enum rather than a simple string (backwards-incompatible)Fix failing tests due to version number0.2.0 (2017-05-01)Alter anime retrieval API to use exceptions (backwards-incompatible)Improve documentation (mainly around the anime API)0.1.0 (2016-05-15)First release on PyPI.
Package: malss
MAchine Learning Support System
###############################malssis a python module to facilitate machine learning tasks.
This module is written to be compatible with thescikit-learn algorithms <http://scikit-learn.org/stable/supervised_learning.html>_ and the other scikit-learn-compatible algorithms... image::https://travis-ci.org/canard0328/malss.svg?branch=master:target:https://travis-ci.org/canard0328/malssDependenciesmalss requires:python (>= 3.9)numpy (>= 1.21.2)scipy (>= 1.7.1)scikit-learn (>= 1.1.1)matplotlib (>= 3.4.3)pandas (>= 1.3.3)jinja2 (>= 3.1.2).. * PyQt5 (== 5.10) (only for interactive mode)All modules except PyQt5 are automatically installed when installing malss.Installationpip install malssFor interactive mode, you need to install PyQt5 using pip.pip install PyQt5ExampleSupervised learningClassification:.. code-block:: pythonfrom malss import MALSS
from sklearn.datasets import load_iris
iris = load_iris()
model = MALSS(task='classification', lang='en')
model.fit(iris.data, iris.target, 'classification_result')
model.generate_module_sample('classification_module_sample.py')Regression:.. code-block:: pythonfrom malss import MALSS
from sklearn.datasets import load_boston
boston = load_boston()
model = MALSS(task='regression', lang='en')
model.fit(boston.data, boston.target, 'regression_result')
model.generate_module_sample('regression_module_sample.py')Change algorithm:.. code-block:: pythonfrom malss import MALSS
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier as RF
iris = load_iris()
model = MALSS(task='classification', lang='en')
model.fit(iris.data, iris.target, algorithm_selection_only=True)
algorithms = model.get_algorithms()check algorithms heremodel.remove_algorithm(0)  # remove the first algorithmadd random forest classifiermodel.add_algorithm(RF(n_jobs=3),
[{'n_estimators': [10, 30, 50],
'max_depth': [3, 5, None],
'max_features': [0.3, 0.6, 'auto']}],
'Random Forest')
model.fit(iris.data, iris.target, 'classification_result')
model.generate_module_sample('classification_module_sample.py')Feature selection:.. code-block:: pythonfrom malss import MALSS
from sklearn.datasets import make_friedman1
X, y = make_friedman1(n_samples=1000, n_features=20, noise=0.0, random_state=0)
model = MALSS(task='regression', lang='en')
model.fit(X, y, dname='default')check the analysis reportmodel.select_features()
model.fit(X, y, dname='feature_selection')You can set the original data after feature selection(You do not need to select features by yourself.)..
Interactive mode:In the interactive mode, you can interactively analyze data through a GUI... code-block:: pythonfrom malss import MALSS

MALSS(lang='en', interactive=True)Unsupervised learningClustering:.. code-block:: pythonfrom malss import MALSS
from sklearn.datasets import load_irisiris = load_iris()
model = MALSS(task='clustering', lang='en')
model.fit(iris.data, None, 'clustering_result')
pred_dict = model.predict(iris.data)
Package: malta
maltaMALTA: Model of Averaged in Longitude Transport in the Atmosphere.Installation$pipinstallmaltaUsagemaltais a two-dimensional (longitudinally-averaged) model of atmospheric transport.
A simple run of constant emissons of 10 Gg of CFC-11 with zero initial conditions
from 2010-2020 inclusive could be:importnumpyasnpfrommaltaimportmodelstart_year=2010end_year=2021dt=8*3600species="CFC11"# Set up and run modelyears=np.array([str(yr)foryrinrange(start_year,end_year)])emistot=np.repeat(10,len(years))emissions=model.create_emissions(species,emistot,dt)sink=model.create_sink(species,dt)ds_out=model.run_model(years,dt,emissions,sink)The returned ds_out is an xarray dataset containing monthly mean output variables from
the 2D model run.
See docs/example.ipynb or thereadthedocspage for more information on running the model.ContributingInterested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.Licensemaltawas created by Luke Western. It is licensed under the terms of the MIT license.Creditsmaltawas created withcookiecutterand thepy-pkgs-cookiecuttertemplate.
Package: maltfy
==========================================================
maltfy  Copyright (C) 2014  F. Brezo and Y. Rubio, i3visio
==========================================================Description:maltfy is a GPLv3+ piece of software conceived to export i3visio OSINT
tools to Maltego.License: GPLv3+This is free software, and you are welcome to redistribute it under
certain conditions.This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.Installation:The installation under Python 2.7 for the release package is as follows:1.- Download and install Maltego (Community Edition is free)2.- Run Maltego. It is not necessary to create an account.3.- We recommend to install the app under a specific folder to let
Maltego find the transforms without further configuration. The
installation under a Linux system with a folder ‘/var/i3visio’ (in
Windows, it should be ‘C:/i3visio’) already created is as follows:# Navigate to the folder cd /var/i3visio git clonehttp://github.com/i3visio/maltfycdmaltfy-mastersudo python setup.py build sudo python setup.py installor# Navigate to the folder cd /var/i3visio wgethttp://github.com/i3visio/maltfy/archive/master.zipunzipmaltfy-master.zipcd master sudo python setup.py build sudo python setup.py installSuperuser privileges are required so as to complete the installation.
Afterwards, the module will be importable from any python code. You can
check this by typing:python-c"import maltfy"If no error is
displayed, the installation would have been performed correctly.4.- Import the i3visio configuration from the
maltfy-master/maltfy/i3visio-configuration.5.- Update the Working directory of the loaded transforms if necessary
to match the appropriate directory. By default it can be found under: -
Linux: /var/i3visio/maltfy-master/maltfy - Windows:
c:/i3visio/maltfy-master/maltfy
Package: mal-tier-list-bbcode-gen
MAL Tier List BBCode Generatormal-tier-list-bbcode-genis a Python package that generates BBCode for
tier lists with custom images corresponding to entries in MAL (characters, anime, manga, people).
Each image is also a link to the respective entry's MAL page.Example character tier listInstallationpip install mal-tier-list-bbcode-genorgit clone https://github.com/juliamarc/mal-tier-list-bbcode-gen
cd mal-tier-list-bbcode-gen
python -m venv venv
source venv/bin/activate
pip install -r requirements.txtFor Windows usevenv\Scripts\activate.batorvenv\Scripts\activate.ps1to activate the virtual environment.User guideTiersEach tier is represented by one sheet of thetiers.odsspreadsheet.
By default there are 7 tiers named 'tier S', 'tier A', 'tier B' ..., 'tier F'.
I provided an example filled out tier in the sheet namedEXAMPLE TIER.HeadersEach tier has a header.
The headers are images.
I provided a default header for each tier intiers.ods(color-coded in the typical tier list style).Some example headers and the.xcffile (GIMP format) that was used to generate them can be found inexample-headers.EntriesEach entry consists of a link to MAL and a link to an image.Image sourceThe images need to be hosted somewhere.
Currently there are two options:upload your images to an image hosting service likePostimagesand use the direct URL, orupload your images to Google Drivecreate a folder for the imagesmake it public ("Anyone with the link can view")use the generated share links ("Get sharable link" for each image)Image sizeIt's best for all the images to be the same size.
MAL's BBCode doesn't allow for resizing, so the desired image size needs to be set before upload.Another tip on image size is to make the header width divisible by the entry image's width so they tile nicely.SettingsBasic settings can be found in theSETTINGSsheet.
Curretnly there are two settings:"Tier order" - list of the tier sheets that will be included in the BBCode and the order in which they will be displayed"Entries per row" - how many entries will be displayed in one rowUsageYou can edit thetiers.odsspreadsheetdirectly or create a copy of it.
I will show an example fortiers.odshere, but if your file is named differently replacetierswith your file's name.Fill outtiers.odsRunmal-tier-list-bbcode-gen tiers.odsif you installed with pip, orpython -m mal_tier_list_bbcode_gen tiers.odsif you usedgit cloneThe BBCode can be found intiers.bbcode.txtand a preview of it is inpreview.html.Adding a tierAdd a sheet to the spreadsheet and add its name to the "Tier order" inSETTINGS.Removing a tierDelete the tier's name from the "Tier order" inSETTINGS.
The sheet doesn't have to be removed from the spreadsheet.
Package: maltk
Model Assisted Labeling ToolkitMalt allows users to quickly create labeled images from a video with the help of a predefined model. Creating good training data for object recognition is an iterative process, and malt is designed to speed up each iteration. The typical malt workflow will go as follows:Create a sparsely trained Tensorflow Lite ModelLoad and scan through a video to find object positions that are unfamiliar to the model and label those framesAdjust the auto-generated bounding boxes to perfectly fit the target objectSave the labeled frames as imagesTrain a new modelLoad the new model and a new videoRepeat the processInstall & Launch Maltpip3 install maltk
malt or python3 -m malt
Package: maltor
Maltor_  _          __    __                 
  /  \/ \ _____  |  | _/  |_  ____ _______ 
 /       \\__  \ |  | \   __\/ __ \\_  __ \
/  /\  |  \/ __ \_  |__|  | (  \_\ )|  | \/
\_/  \/|  /____  /____/|__|  \____/ |__|   
        \/     \/                          
v1.1.2Maltor is A CLI tool for static malware analysis written in Python
Package: maltose
Maltose一个使用Django编写的静态博客生成器。拥有如下功能内置图片管理Makrdown编辑器 in web快速开始pip install maltose使用在你的博客目录下运行命令maltose-cli migrate用以创建数据库，如果目录下没有maltose.json配置，会询问你之后自动创建。博客目录下必须存在templates，其中包含articles与static，前者包含所有页面的模板，后者包含所需要的静态文件。(可以使用现成的模板, 例如:https://github.com/MaltoseEditor/black-white)使用maltose-cli.py runserver，你将能启动Django程序，你可以在localhost:8000/editor/中获得一个编辑器，用以书写博客。程序使用Django信号来创建用以发布到网络上的静态页面。高级使用你可以直接下载源码然后依照标准Django程序进行修改，使用python setup.py install可以安装你自己改修后的版本。更多说明……to be continue
Package: maltsev-repeater
Maltsev.RepeaterUsage Repeater.Next and returns string:calls_count:int=0defaction(multiplier:int)->str|Repeater.Next:globalcalls_countcalls_count+=1ifcalls_count==5:return'Hello World!'else:print(f'calls_count:{calls_count}, result:{multiplier*calls_count}')returnRepeater.Next()repeater=Repeater(action=action)repeater.configure(attempts=10,delay=0.2)result=repeater.run(multiplier=2)ifresult.is_success:print(result.value)# output:# -> calls_count: 1, result: 2# -> calls_count: 2, result: 4# -> calls_count: 3, result: 6# -> calls_count: 4, result: 8# -> Hello World!Usage Repeater.Next and Repeater.Fail:calls_count:int=0defaction(multiplier:int)->Repeater.Next|Repeater.Fail:globalcalls_countcalls_count+=1ifcalls_count==5:returnRepeater.Fail(message='failed abc')else:print(f'calls_count:{calls_count}, result:{multiplier*calls_count}')returnRepeater.Next()repeater=Repeater(action=action)repeater.configure(attempts=10,delay=0.2)result=repeater.run(multiplier=2)ifresult.is_success:# not success, because action returns Repeater.Failprint('is success statement')print(result.value)ifresult.is_failed:print('is failed statement')print(result.error_message)# output:# -> calls_count: 1, result: 2# -> calls_count: 2, result: 4# -> calls_count: 3, result: 6# -> calls_count: 4, result: 8# -> is failed statement# -> failed abcUsage Repeater.Next and raise AssertionError:calls_count:int=0defaction(multiplier:int)->Repeater.Next:globalcalls_countcalls_count+=1ifcalls_count==5:raiseAssertionError('assertion error abc')else:print(f'calls_count:{calls_count}, result:{multiplier*calls_count}')returnRepeater.Next()repeater=Repeater(action=action)repeater.configure(attempts=10,delay=0.2)try:_=repeater.run(multiplier=2)exceptAssertionErroraserror:print(error)# output:# -> calls_count: 1, result: 2# -> calls_count: 2, result: 4# -> calls_count: 3, result: 6# -> calls_count: 4, result: 8# -> assertion error abc
Package: malware-atlas
ATLASAccording toMerriam-Webster:atlasnounat·​las|at-ləs1. capitalized :a Titan who for his part in the Titans' revolt against the gods is forced by Zeus to support the heavens on his shoulders3. a :a bound collection of maps often including illustrations, informative tables, or textual matterATLASis an analysis description of malware or kill-chain. Malware is a combination of techniques crafted for a purpose. With an ATLAS rule, these techniques and capabilities are like LEGO pieces. In this way, it tries to help malware researchers to focus single piece at a time and nothing more. ATLAS interpretation of the rule does the rest. It also removes the language boundaries. Different pieces can be written in other script languages.If these techniques are transformed into LEGO pieces properly, it eventually creates a memory. Then, the total time to write an ATLAS rule will decrease.meta:name:"rtf_template_injection"description:"Aruletoextractsrtftemplateinjection"reference:"https://www.proofpoint.com/us/blog/threat-insight/injection-new-black-novel-rtf-template-inject-technique-poised-widespread"version:"1.0"scripts:# import re# import base64# def run(data: bytes) -> str:#     result = ''#     encoded_template_pattr = "XHtcXFwqXFx0ZW1wbGF0ZVxzKyguKylccypcfQ=="#     result = re.search(base64.b64decode(encoded_template_pattr), data).group(1).decode()#     return results1:"aW1wb3J0IHJlCmltcG9ydCBiYXNlNjQKIApkZWYgcnVuKGRhdGE6IGJ5dGVzKSAtPiBzdHI6CiAgICByZXN1bHQgPSAnJwoKICAgIGVuY29kZWRfdGVtcGxhdGVfcGF0dHIgPSAiWEh0Y1hGd3FYRngwWlcxd2JHRjBaVnh6S3lndUt5bGNjeXBjZlE9PSIKICAgIHJlc3VsdCA9IHJlLnNlYXJjaChiYXNlNjQuYjY0ZGVjb2RlKGVuY29kZWRfdGVtcGxhdGVfcGF0dHIpLCBkYXRhKS5ncm91cCgxKS5kZWNvZGUoKQoKICAgIHJldHVybiByZXN1bHQ="chain:file_read:input:$param.filefunc:file_read_bintemplate_extract:input:-$scripts.s1-$file_readfunc:python_executordownload:input:$template_extractfunc:download_from_remote_serversave_template:input:-$download-"template_"func:save_file_bytesWhen the above rule is processed by ATLAS:It reads the file according to command-line argument,Then runs the python script that is defined inscriptssection,Tries to downloads the template from the matched pattern,And saves the downloaded data to the disk.InstallationInstall using Python's PIP:pipinstallmalware-atlasClone directly from Github:gitclonehttps://github.com/malware-atlas/atlasUsageTheHelloWorldrule can be used to test the installation.To test it as a stand-alone tool:atlas-aHelloWorld.atlOr it can be used as a package:>>>fromatlasimportatlas
>>>atl=atlas("HelloWorld.atl")>>>atl.execute()TrueTo discover the full potential of the ATLAS, you could check the documentation:https://malware-atlas.readthedocs.io/en/latest/
Package: malwareDetector
malwareDetectorSource code:https://github.com/louiskyee/malwareDetector.gitWiki:https://github.com/louiskyee/malwareDetector/wikiPyPI:https://pypi.org/project/malwareDetector/DescriptionThis is a malware detector specification for NTUST isLab.
ThemalwareDetectoris a base class designed for malicious software detection. It enables straightforward utilization of Python's inheritance feature. By inheriting frommalwareDetectorand implementing the required functions, you can achieve your specific goals. Additionally, it offers convenient configuration management. For more detailed instructions, please refer to theGitHub Wiki.RequirementsToolVersionSourcePython>= 3.10https://www.python.org/downloadsInstallationUse the package managerpipto installmalwareDetector.Example:pip install malwareDetectorUsageimportimport classdetectorfrommalwareDetector.detectorfrom malwareDetector.detector import detectorExamples:from malwareDetector.detector import detector
from typing import Any
import numpy as np

class subDetector(detector):
    def __init__(self) -> None:
        super().__init__()

    def extractFeature(self) -> Any:
        return 'This is the implementation of the extractFeature function from the derived class.'

    def vectorize(self) -> np.array:
        return 'This is the implementation of the vectorize function from the derived class.'

    def model(self) -> Any:
        return 'This is the implementation of the model function from the derived class.'

    def predict(self) -> np.array:
        return 'This is the implementation of the predict function from the derived class.'
Package: mamaprofile
PyPi:https://pypi.org/project/mamaprofile/Python OOP+ วิธีสร้าง Library เป็นของตัวเอง+ อัพโหลด Package ไปยัง
PyPI.orgโปรแกรมนี้ใช้สำหรับสอนการเขียนโปรแกรมแบบ OOPวิธีติดตั้งเปิด CMD / Terminalpipinstallmamaprofileวิธีใช้งานแพ็คเพจนี้เปิด IDLE ขึ้นมาแล้วพิมพ์…```python from mamaprofile import profileprint('start')
me = profile('Mama')
print(me)
print(me.name)
me.company = 'ABC'
me.show_email()
print('----------------')

me.hobby = ['Youtuber','Reading','Sleeping']
me.show_hobby()

other = profile('Papa')
print(other)
print(other.name)
other.show_email()

art = other.show_ascii_art()
print(art)
print('\n',other.show_ascii_art())พัฒนาโดย: mamaprofile