AbdouEnsaj's repositories
PFE
Construction d'un modèle ML pour détecter les courriels de phishing à l'aide des techniques NLP et virusTotal
A Really Ruby Mail Library
Malicious_URL_Analyzer
A tool that detects maliciousness of suspicious links, written in python with <3.
NLP4CyberSecurity
NLP model and tech for cyber security tasks
Phishing-URL-Detection
Phishers use the websites which are visually and semantically similar to those real websites. So, we develop this website to come to know user whether the URL is phishing or not before using it. URL - http://phishing-url-detector-api.herokuapp.com/
DATA_D3.JS
Data utilisé dans les exemples du rapport de stage
Automating-VirusTotal-APIv3-for-IPs-and-URLs
Automating VirusTotal's API v3 for IP address and URL analysis w/HTML Reporting. Python script that functions like a CLI tool to interact programmatically with VirusTotal API v3.
WordEmb-DL_EmailPhishing_Detection
Dataset Availabilty for Application of Word embedding and Deep learning in detecting phishing emails
Phishing-Detection
Phishing Detection System using Natural Language Processing and Machine Learning
Detecting-Phishing-Attack-using-ML-DL-Models
Developed a model to detect Phished emails from legitimate ones using the Spam Assassin dataset. Extracted relevant features by processing the mails using the NLP toolkit. Built various ML models like Naïve Bayes, Random Forest, and Voting Ensemble with the best accuracy of ~72%, and deep learning model like Neural Network with an accuracy of ~
Spoon-Knife
This repo is for demonstration purposes only.
enron_spam_data
The Enron-Spam dataset preprocessed in a single, clean csv file.
mailMeta
An forensics tool to help aid in the investigation of spoofed emails based off the email headers.
Practical_Security
Average Height of 10 people using Python
NLP-Phish-Dissertation-
Hons project deep learning at detecting phishing emails based form plain text CNN,BLSTM, BiCnn and a LSTM
phishingdata-Analysis
Experimentation with Sentiment Analysis on Phishing Email Datasets. Machine-learning techniques to help classify the overall emotional content of the data as well as the difference among different phishing data