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A detailed comparison of performance scores achieved by Machine Learning and Deep Learning algorithms on 3 different Phishing datasets. 3 different feature selection and 2 different dimensionality reduction techniques are used for comparison.
Experiments to detect phishing websites using neural networks
Phishing websites are fraudulent sites that impersonate a trusted party to gain access to sensitive information of an individual person or organization. Traditionally, phishing website detection is done through the usage of blacklist databases. However, due to the current, rapid development of global networking and communication technologies, there are numerous websites and it has become difficult to classify based on traditional methods since new websites are created every second. In this paper, we are proposing a real-time, anti-phishing system. In the first step, we extract the lexical and host-based properties of a website. In the second step, we combine URL (Uniform Resource Locator) features, NLP and host-based properties to train the machine learning and deep learning models. Our detection model is able to detect phishing URLs with a detection rate of 94.89%.
Full-featured C2 framework which silently persists on webserver with a single-line PHP backdoor
More than twenty practical cases with real datasets of application of Machine Learning to the field of Cybersecurity
Binaries for the book Practical Malware Analysis
PEASS - Privilege Escalation Awesome Scripts SUITE (with colors)
A python module for retrieving and parsing WHOIS data
Portable executable file malware detection with PyTorch
Tutorials on getting started with PyTorch and TorchText for sentiment analysis.
Here I show with a Recurrent Neural Network (RNN) model how fast and uncomplicated a model can be calculated that can distinguish spam from non-spam
Implement Differentiable Architecture Search (DARTS) for RNN with fastai
Hyperparameter optimization that enables researchers to experiment, visualize, and scale quickly.
The Social-Engineer Toolkit (SET) repository from TrustedSec - All new versions of SET will be deployed here.
VIP cheatsheets for Stanford's CS 230 Deep Learning
Python-based utility that uses supervised machine learning to detect phishing domains from the Certificate Transparency log network.
train and predict log message with deep learning methods
用TensorFlow搭建CNN/RNN/LSTM/GRU/BiRNN/BiLSTM/BiGRU/Capsule Network等deep learning模型
Text Generation using Bidirectional LSTM and Doc2Vec models
Project analyzes Amazon Stock data using Python. Feature Extraction is performed and ARIMA and Fourier series models are made. LSTM is used with multiple features to predict stock prices and then sentimental analysis is performed using news and reddit sentiments. GANs are used to predict stock data too where Amazon data is taken from an API as Generator and CNNs are used as discriminator.
zeus-style banking trojan
Botnet system from deep-web Russians forums. Tiny Nuke!
Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning (NeurIPS 2020)
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.