惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

aimingoo的专栏
aimingoo的专栏
WordPress大学
WordPress大学
阮一峰的网络日志
阮一峰的网络日志
博客园 - 司徒正美
月光博客
月光博客
宝玉的分享
宝玉的分享
Recent Announcements
Recent Announcements
小众软件
小众软件
H
Hackread – Cybersecurity News, Data Breaches, AI and More
美团技术团队
博客园 - 三生石上(FineUI控件)
A
About on SuperTechFans
J
Java Code Geeks
云风的 BLOG
云风的 BLOG
罗磊的独立博客
大猫的无限游戏
大猫的无限游戏
IT之家
IT之家
Vercel News
Vercel News
量子位
Martin Fowler
Martin Fowler
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
V
Visual Studio Blog
腾讯CDC
有赞技术团队
有赞技术团队

DEV Community

Authentication Security Deep Dive: From Brute Force to Salted Hashing (With Java Examples) Why AI Systems Don’t Fail — They Drift Spilling beans for how i learn for exam😁"Reinforcement Learning Cheat Sheet" I Replaced Chrome with Safari for AI Browser Automation. Here's What Broke (and What Finally Worked) How Python Borrows Other People's Work The $40 Architecture: Processing 1 Billion API Requests with 99.99% Uptime Vibe Coding: A Workflow Guide (From Zero to SaaS) Most webhook security guides protect the wrong side. The scary part is delivery. Headless CMS for TanStack Start: Build a Blog with Cosmic EU Age Verification App "Hacked in 2 Minutes" — What Actually Happened Comfy Cloud’s delete function does not actually remove files Running AI Models on GPU Cloud Servers: A Beginner Guide Event-driven media intelligence with AWS Step Functions and Bedrock I scored 500 AI prompts across 8 quality dimensions — here's what broke How to Call Google Gemini API from Next.js (Free Tier, No Backend Needed) The Portal Protocol: Reclaiming Human Connection in the Age of AI How to Fix Your Team's Scattered Knowledge Problem With a Self-Hosted Forum Intro to tc Cloud Functors: A Graph-First Mental Model for the Modern Cloud Designing Multi-Tenant Backends With Both Ownership and Team Access I Built a Neumorphic CSS Library with 77+ Components — Here's What I Learned PostgreSQL Performance Optimization: Why Connection Pooling Is Critical at Scale Cómo construí un SaaS multi-rubro para gestionar expensas en Argentina con FastAPI + Vue 3 🚀 I Built an Ethical Hacking Scanner Tool – Open Source Project I Replaced /usage and /context in Claude Code With a Single Statusline A Pythonic Way to Handle Emails (IMAP/SMTP) with Auto-Discovery and AI-Ready Design I Collected 8.9 Million Polymarket Price Points — Here's What I Found About How Markets Really Move EcoTrack AI — Carbon Footprint Tracker & Dashboard Everyone's Using AI. No One Agrees How. 5 self-hosted ebook managers worth trying in 2026 Building Your First AI Agent with LangChain: From Chatbot to Autonomous Assistant
Catching Hackers with Math: How I Built a Self-Healing Se...
Ajibola Anjo · 2026-04-28 · via DEV Community

If you’ve never worked in cybersecurity before, the word "DevSecOps" sounds intimidating. It sounds like you need to be in a dark room wearing a hoodie, typing furiously to stop hackers.

But in reality? Good security isn't about typing fast. It’s about building smart alarms.

For my latest engineering project, I built an Anomaly Detection Engine from scratch. Here is a beginner-friendly breakdown of how I used simple math to teach a server to defend itself.

The Problem: Hard-Coded Rules Fail
Imagine you run a popular online store. You tell your bouncer (your firewall): "If anyone tries to enter the store more than 10 times a second, kick them out! They must be a hacker doing a brute-force attack."

That works great on a normal Tuesday. But what happens on Black Friday? Suddenly, hundreds of real customers are rushing the doors. Your bouncer kicks them all out, and your business crashes. Hard-coded limits don't adapt to reality.

The Solution: The "Resting Heartbeat"
Instead of a strict rule, my security engine calculates a Rolling Baseline. Think of this as the server's resting heartbeat.

Every single minute, a background script looks at the traffic and says, "Okay, right now, we are averaging about 1 request per second." If traffic slowly builds up over the afternoon (like a Black Friday sale), the baseline adjusts to accept it as the new normal.

The Trigger: The Z-Score (The Conveyor Belt)
To catch actual attacks, the engine uses a 60-second "Sliding Window"—like a conveyor belt of incoming traffic.

It tracks every IP address on that belt and compares them to our baseline heartbeat using a mathematical formula called a Z-Score. A Z-Score tells us exactly how "weird" a spike in traffic is.

In my engine, the alarm triggers if an IP hits a Z-Score of 3.0. In the world of statistics, anything past a 3.0 means there is a 99.7% chance that this spike is a massive anomaly, not just an enthusiastic user.

(During testing, I accidentally triggered a Z-Score of 40.17! The engine didn't hesitate.)

The Trapdoor: Auto-Banning and Slack Alerts
When the math catches an attacker, the engine doesn't wait for a human to respond. It takes immediate action:

The Block: It talks directly to the server's core firewall (iptables) and drops all network traffic from that specific IP address instantly.

The Alert: It sends a formatted alert directly to my phone via Slack, showing me the attacker's IP and how hard they tried to hit the server.

The Recovery: It starts a 10-minute timer. When the timer expires, it automatically unbans the IP. This ensures that if a real user's device just glitched out, they aren't permanently banned forever.

Conclusion
Building this taught me that modern security isn't just about building taller walls; it’s about building smarter sensors. By combining simple statistics with automated firewalls, you can build a server that heals itself while you sleep!