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Malicious URL Detection
Static Malware Classification Using Deep Learning
Classify a file as legitimate or malware based on given attributes of file using Deep Neural Network
Using Deep Learning to classify malware
Firstly, we generate images from benign and malware executable files. Secondly, by using deep learning, we train a model to detect malware files. Then, by the trained model, we try to classify a file as malware or not. By using malware images and deep learning, we can detect malware fast since we do not need any static analysis or dynamic analysis.
Multi-class malware classification using Deep Learning
An approach to detect Malware Files using Deep Learning
Materials for Windows Malware Analysis training (volume 1)
Mask R-CNN
MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.
The NetHack Learning Environment
Normalize a URL
DEF CON 27 (2019) OpenCTF Repository - Developed, Organized, and Hosted by Neg9
Collection of things made during my OSCP journey
Materials for OSCP exam
Modified template for the OSCP Exam and Labs. Used during my passing attempt
:orange_book: Markdown Templates for Offensive Security OSCP, OSWE, OSCE, OSEE, OSWP exam report
All in One OSCP Preparation Material
This is my cheatsheet and scripts developed while taking the Offensive Security Penetration Testing with Kali Linux course.
This is a collection of resources, scripts, bookmarks, writeups, notes, cheatsheets that will help you in OSCP Preparation as well as for general pentesting and learning. If you feel like you can contribute in it. Please do that, I'll appreciate you.
A list of commands, scripts, resources, and more that I have gathered and attempted to consolidate for use as OSCP (and more) study material. Commands in 'Usefulcommands' Keepnote. Bookmarks and reading material in 'BookmarkList' CherryTree. Reconscan Py2 and Py3. Custom ISO building.
Conference Papers and Appendicies (USENIX Security, BlackHat, HITBSecConf, and BeVX)
Phishing dataset with more than 88,000 instances and 111 features. Web application available at. https://gregavrbancic.github.io/Phishing-Dataset/
Phishing website detection system provides strong security mechanism to detect and prevent phishing domains from reaching user. This project presents a simple and portable approach to detect spoofed webpages and solve security vulnerabilities using Machine Learning. It can be easily operated by anyone since all the major tasks are happening in the backend. The user is required to provide URL as input to the GUI and click on submit button. The output is shown as “YES” for phishing URL and “NO” for not phished URL. PYTHON DEPENDENCIES: • NumPy, Pandas, Scikit-learn: For Data cleaning, Data analysis and Data modelling. • Pickle: For exporting the model to local machine • Tkinter, Pyqt, QtDesigner: For building up the Graphical User Interface (GUI) of the software. To avoid the pain of installing independent packages and libraries of python, install Anaconda from www.anaconda.com. It is a Python data science platform which has all the ML libraries, Data analysis libraries, Jupyter Notebooks, Spyder etc. built in it which makes it easy to use and efficient. Steps to be followed for running the code of the software: • Install anaconda in the system. • gui.py : It contains the code for the GUI and is linked to other modules of the software. • Feature_extractor.py: It contains the code of Data analysis and data modelling. • Rf_model.py: It contains the trained machine learning model. • Only gui.py is to be run to execute the whole software.
It is a project of detecting phishing websites which are main cause of cyber security attacks. It is done using Machine learning with Python
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.