CyberAura - Analytics platform that identifies URL-based attac

项目说明

🛡️ CyberAura: Hybrid URL Attack Detection Engine

![Live App]()

CyberAura is a web-based security tool designed to identify URL-based attacks from network traffic data (PCAP files) or log files (CSV). It le a powerful hybrid detection engine that combines high-speed pattern matching with intelligent machine learning to provide comprehensi analysis.

🚀 Live Demo

You can access and test the li here:

✨ Core Features & Methodology

The prototype implements the core features outlined in our initial proposal.

Hybrid Detection Engine

Utilizes a two-phase approach for maximum accuracy:

Phase 1: Regex Engine: A high-speed scanner using specific, curated patterns to find known attacks that are directly in the URL (e.g., ' OR 1=1).

Phase 2: Machine Learning Model: A trained Random Forest classifier that identifies complex or hidden attacks by analyzing URL features (length, entropy, character patterns), even when the malicious payload isn't obvious.

Multi-Format Support

Ingests and analyzes both raw network traffic (.pcap) and pre-parsed log files (.csv).

Comprehensi Co

The prototype is trained to detect the most common URL-based threats:

SQL Injection (SQLi)

Cross-Site Scripting (XSS) - Stored, Reflected, and DOM-based

Command Injection

File Inclusion

Interacti

A user-friendly web interface built with Streamlit that provides a clear and immediate summary of the analysis, including metrics, charts, and a detailed, color-coded transaction log.

🛠️ Technology Stack

Backend

Python

Data Processing: Pandas

Network Analysis: Pyshark

Machine Learning: Scikit-learn (Random Forest, Tfidf), Joblib

Frontend

Streamlit

Plotting: Plotly Express

项目信息

开发语言：Python

Star 数：6

技术要点

该项目属于防护思路方向的开源资源，代码结构清晰，适合安全研究人员学习参考。项目围绕Analytics platform that identifies URL-based attacks from IP data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。