ids-aiml - 🕵️ IDS with accuracy rates of 94.4% for misuse det

项目说明

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ids-aiml 🕵️

!Detective GIF

An IDS designed to detect network attacks using decision trees and neural networks.

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Project O

This GitHub repository contains the code and documentation for an Intrusion Detection System (IDS) de by Derek Chan and Oli under the super of Dr. Leon Reznik. The IDS is designed to detect network attacks using decision trees and neural networks.

Usage Steps

Step 1: Clone the Repository

Clone this GitHub repository to your local machine:

Step 2: Na to the Project Directory

Step 3: Explore the Project Structure

Take a moment to explore the project structure as well as the Weka user guide. Read the steps in the "Data preparation" and "Operation" sections to know how we preprocessed the data using the provided Python script (preprocessing.py).

Step 4: Set Up En

Ensure you ha installed along with the required libraries (pandas, numpy, sklearn, concurrent.futures). Weka is also required for the neural network module.

Step 5: Misuse IDS

#### Design

#### Operation

Step 6: Anomaly IDS

#### Design

#### Operation

Step 7: Neural Network

#### Design

#### Operation

Step 8: Data Analysis with Weka

Step 9: Tests and Results

Additional Information

For any questions or issues, please feel free to reach out to the project contributors listed in the README. Thank you for your interest in our intrusion detection system project!

Releases

* 1.0.0-alpha - testing is done by de only

Acknowledgements

* Derek Chen () -
* Oli (@oli) -

Warranty

MIT

The creator(s) of this tool provides no warranty or assurance regarding its performance, dependability, or suitability for any specific purpose.

The tool is furnished on an "as is" basis without any form of warranty, whether express or implied, encompassing, but not limited to, implied warranties of merchantability, fitness for a particular purpose, or non-infringement.

The user assumes full responsibility for employing this tool and does so at their own peril. The creator(s) holds no accountability for any loss, damage, or expenses sustained by the user or any third party due to the utilization of this tool, whether in a direct or indirect manner.

Moreover, the creator(s) explicitly renounces any liability or responsibility for the accuracy, substance, or a of information acquired through the use of this tool, as well as for any harm inflicted by , malware, or other malicious components that may infiltrate the user's system as a result of employing this tool.

By utilizing this tool, the user acknowledges that they ha and understood this warranty declaration and agree to undertake all risks linked to its utilization.

License

This project is licensed under the MIT License - see the LICENSE.md file for details

项目信息

开发语言：Python

Star 数：15

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🕵️ IDS with accuracy rates of 94.4% for misuse detection and 99.0% for anomaly detection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。