WhiteHat - A collection of AI-powered tools for phishing dete

项目说明

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WhiteHat

Welcome to the WhiteHat repository!

This project is a collection of resources, guides, and research in cybersecurity and artificial intelligence. The materials are organized into different sections to help students, professionals, and enthusiasts expand their knowledge and practical skills in areas of cybersecurity.

Repository Structure

AI Projects

Experiments and practical projects applying artificial intelligence and machine learning to cybersecurity problems, such as:
- Ad machine learning (e.g., data poisoning, e attacks)
- Automated malware detection
- Security analytics using deep learning

Research

Open source projects, articles, and presentations from my cybersecurity research.

Information Gathering

- Fingerprinting techniques and tools
- Automated tools for reconnaissance

My Courses

Networks and lab

- Club Hack1ng
- Workshops, tutorials, and learning experiences
- Social engineering and web security
- Programming with Python

Explore each folder for hands-on scripts, educational content, and research insights. All materials are intended for ethical and educational use.

Highlights

Information Gathering

Explore techniques for gathering information about a target, including manual and automated tools. Learn about documentation tools, network reconnaissance, and how to interpret data from HTTP requests and cookies. Example tools include:

Netcat

Telnet

Nmap

WhatWeb

Httpprint

BlindElephant

Club Hack1ng

A space for sharing educational acti and experiences from the Hack1ng Club at Uni Icesi. Topics include:

Social Engineering tools and concepts

Web security practices

Python programming for cybersecurity

Workshop materials and practical exercises

Disclaimer

> This repository is intended for educational purposes only. All information and tools provided here are intended for responsible and ethical use.

References

+ Seitz, Justin. Black Hat Python: Python Programming for Hackers and Pentesters. No Starch Press, 2014.
+ Ramos, Antonio., Barberto, C. Marugan, D., & Gonzáles I. hacking con ingeniería social técnicas para hackear humanos
+ Hadnagy, C. (2014). Unmasking the social engineer: The human element of security. John Wiley & Sons.
+ Ramos, Antonio., Barberto, M. Rubén., G. Ángel., & Gonzáles I. Hacking y seguridad de páginas web
+ Lutz, M. (2013). Learning Python: Powerful Object-Oriented Programming. " O'Reilly Media, Inc.".
+ O'Connor, T. J. (2012). Python: a cookbook for hackers, forensic analysts, penetration testers and security engineers. Newnes.

Feel free to explore the subdirectories for more detailed guides and resources!

项目信息

开发语言：Jupyter Notebook

Star 数：120

技术要点

该项目属于红蓝攻防方向的开源资源，代码结构清晰，适合安全研究人员学习参考。项目围绕A collection of AI-powered tools for phishing detection, adversarial machine learning, and cybersecurity research. Includes Streamlit/Flask apps, attack simulations, LoRA fine-tuning, and secure model evaluation for red teaming and defense.展开，提供了可运行的实现，对理解相关安全机制、工具原理与攻防技术具有较高的研究价值。

从技术实现角度看，此类项目的核心在于对底层原理的深入理解与工程化落地。开发者在研究过程中可以结合官方文档与社区讨论，梳理项目的初始化流程、核心模块职责与数据流转，逐步掌握其中的技术细节与设计思路。

使用与学习建议

1. 下载源码压缩包，解压到本地环境。

2. 根据项目类型准备对应的运行环境与依赖。

3. 按照 README 中的步骤完成配置与部署。

4. 在隔离的测试环境中运行验证，避免影响生产系统。

免责声明

本资源来源于开源社区，仅供安全研究、技术学习与合法授权范围内的攻防演练使用，请勿用于任何非法或侵害他人权益的用途。使用本项目产生的一切后果由使用者自行承担。