<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Asset Discovery on Mi&amp;Bee Blog</title><link>https://blog.mickeyzzc.tech/en/tags/asset-discovery/</link><description>Recent content in Asset Discovery on Mi&amp;Bee Blog</description><generator>Hugo -- gohugo.io</generator><language>en</language><managingEditor>蓝宝石的傻话</managingEditor><lastBuildDate>Thu, 09 Jul 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://blog.mickeyzzc.tech/en/tags/asset-discovery/rss.xml" rel="self" type="application/rss+xml"/><item><title>Multi-Fingerprint Aggregation Engines: Survey of fingers, Kscan, Nuclei and 7 Projects</title><link>https://blog.mickeyzzc.tech/en/posts/network/multi-fingerprint-aggregation-engines/</link><pubDate>Thu, 09 Jul 2026 00:00:00 +0000</pubDate><guid>https://blog.mickeyzzc.tech/en/posts/network/multi-fingerprint-aggregation-engines/</guid><description>&lt;p&gt;In network security, fingerprinting is the foundational step for asset discovery and attack surface management. However, a single fingerprint library often has limited coverage—Nmap excels at network-layer service detection but falls short on web technology stacks; Wappalyzer is strong at frontend framework detection but cannot sense underlying protocols; WhatWeb identifies CMS accurately but lacks port scanning capabilities. In practice, a single target may involve network devices, web applications, cloud services, and other asset types simultaneously, so relying on just one fingerprint library inevitably leads to significant omissions.&lt;/p&gt;</description></item></channel></rss>