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phishing-website-detection-by-machine-learning-techniques

phishing-websites-classification-using-deep-learning

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.

phishing-websites-detection

Experiments to detect phishing websites using neural networks

phishing_attack_detection_with_naturallanguageprocessing

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%.

phishing_detection

phishingwebsitedetectionusingdeeplearning

php-url-shortener

phpsploit

Full-featured C2 framework which silently persists on webserver with a single-line PHP backdoor

practical-ml-for-cybersecurity

More than twenty practical cases with real datasets of application of Machine Learning to the field of Cybersecurity

practicalmalwareanalysis-labs

Binaries for the book Practical Malware Analysis

privilege-escalation-awesome-scripts-suite

PEASS - Privilege Escalation Awesome Scripts SUITE (with colors)

projects_ds

python-whois

A python module for retrieving and parsing WHOIS data

pytorch-malware-detection

Portable executable file malware detection with PyTorch

pytorch-sentiment-analysis

Tutorials on getting started with PyTorch and TorchText for sentiment analysis.

rnn-spam-detection-lstm

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

rnn_darts_fastai

Implement Differentiable Architecture Search (DARTS) for RNN with fastai

sherpa

Hyperparameter optimization that enables researchers to experiment, visualize, and scale quickly.

social-engineer-toolkit

The Social-Engineer Toolkit (SET) repository from TrustedSec - All new versions of SET will be deployed here.

spic

stanford-cs-230-deep-learning

VIP cheatsheets for Stanford's CS 230 Deep Learning

streamingphish

Python-based utility that uses supervised machine learning to detect phishing domains from the Certificate Transparency log network.

system_log_lstm_cnn

train and predict log message with deep learning methods

tensorflow-deep-learning

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

Text Generation using Bidirectional LSTM and Doc2Vec models

time-series-forecasting-of-amazon-stock-prices-using-neural-networks-lstm-and-gan-

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.

tinynuke

zeus-style banking trojan

tinynuke-1

Botnet system from deep-web Russians forums. Tiny Nuke!

trajectory_mcl

Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning (NeurIPS 2020)

trojai-literature