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ARL(Asset Reconnaissance Lighthouse)资产侦察灯塔系统旨在快速侦察与目标关联的互联网资产，构建基础资产信息库。 协助甲方安全团队或者渗透测试人员有效侦察和检索资产，发现存在的薄弱点和攻击面。
ARL官方仓库备份项目：ARL(Asset Reconnaissance Lighthouse)资产侦察灯塔系统旨在快速侦察与目标关联的互联网资产，构建基础资产信息库。 协助甲方安全团队或者渗透测试人员有效侦察和检索资产，发现存在的薄弱点和攻击面。
ARL 资产侦察灯塔系统（可运行，添加指纹，提高并发，升级工具及系统，无限制修改版） | ARL(Asset Reconnaissance Lighthouse)资产侦察灯塔系统旨在快速侦察与目标关联的互联网资产，构建基础资产信息库。 协助甲方安全团队或者渗透测试人员有效侦察和检索资产，发现存在的薄弱点和攻击面。
基于ARL v2.6.2版本源码，生成docker镜像进行快速部署，同时提供七千多条指纹
The Model and ObservatioN Evaluation Toolkit (MONET)
The U.S. Army Research Laboratory (ARL) Software Release Process for Unrestricted Public Release
Association Rule Learning via Apriori Algorithm in Python
Association Rule Learning, Content Based Recommendation, Item Based Collaborative, Filtering User Based Collaborative Filtering, Model Based Matrix Factorization projects i've done about
在原有ARL的基础上添加对IP资产的搜索测试支持（还没做）， 目前作为备份仓库使用，更改了原仓库被删除的脚本地址以确保可以正常使用。
Recommend a new product to the customer with association rule learning
Association Rule Learning project on online_retail_II dataset, you can read the readme file for the details of the project. You can find the link of the dataset in the codes.
By examining the products that customers purchase together, we will provide recommendations to similar shoppers. This is a data mining approach that can be used to enhance customer satisfaction and increase sales.
Armut, Turkey's largest online service platform, connects service providers with customers looking for services such as cleaning, renovation, and transportation. Armut aims to create a product recommendation system using Association Rule Learning based on customer service usage and categories.