cloudfast-bit Goto Github PK

m_u_d

Malicious URL Detection

malware-classification

Static Malware Classification Using Deep Learning

malware-classification-using-deep-learning

Classify a file as legitimate or malware based on given attributes of file using Deep Neural Network

malware-classifier

Using Deep Learning to classify malware

malware-detection-using-deep-learning

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.

malware-detection-using-machine-learning

Multi-class malware classification using Deep Learning

malware_detection_using_deep_learning

An approach to detect Malware Files using Deep Learning

malware_training_vol1

Materials for Windows Malware Analysis training (volume 1)

mask-faster-rcnn

Mask R-CNN

mmdnn

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.

my-website

mybook

mybookscollection

nle

The NetHack Learning Environment

normalize-url

Normalize a URL

openctf-2019

DEF CON 27 (2019) OpenCTF Repository - Developed, Organized, and Hosted by Neg9

oscp

Collection of things made during my OSCP journey

oscp-1

Materials for OSCP exam

oscp-exam-report-template

Modified template for the OSCP Exam and Labs. Used during my passing attempt

oscp-exam-report-template-markdown

:orange_book: Markdown Templates for Offensive Security OSCP, OSWE, OSCE, OSEE, OSWP exam report

oscp-preparation-material

All in One OSCP Preparation Material

oscp-pwk-1

This is my cheatsheet and scripts developed while taking the Offensive Security Penetration Testing with Kali Linux course.

oscp_bible

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.

oscprepo-1

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.

pandas-exercises

papers

Conference Papers and Appendicies (USENIX Security, BlackHat, HITBSecConf, and BeVX)

phishing-dataset

Phishing dataset with more than 88,000 instances and 111 features. Web application available at. https://gregavrbancic.github.io/Phishing-Dataset/

phishing-url-classification

phishing-url-detection

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.

phishing-website-detection

It is a project of detecting phishing websites which are main cause of cyber security attacks. It is done using Machine learning with Python