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Build a DDoS Attack Detection and Prevention System

Monitor and analyze network traffic to detect Distributed Denial of Service attacks in real-time and apply automated mitigation strategies — a crucial cybersecurity defense project.

Why Detect and Prevent DDoS Attacks?

DDoS attacks can cripple websites, servers, and online platforms by overwhelming them with traffic from multiple sources. An early detection and response system helps mitigate damage, preserve availability, and reduce downtime for mission-critical services.

Core Features of the System

This system monitors incoming traffic, detects abnormal surges or repeated requests from distributed sources, and takes action through rate limiting, IP blacklisting, or service scaling. It distinguishes between legitimate spikes and malicious floods using rules or AI.

Key Features to Implement

Real-Time Traffic Monitoring

Capture and log incoming request rates, connection attempts, and source IP patterns.

Anomaly Detection Engine

Use threshold rules or ML models to flag sudden spikes in traffic and unusual access behavior.

Mitigation Actions

Apply IP blocking, geo-filtering, rate limiting, or redirect suspicious traffic to honeypots.

Dashboard with Alerting

Visualize attack metrics and send alerts via email, Slack, or SMS when threats are detected.

How the System Works

The system captures real-time HTTP, TCP, or UDP traffic using sniffers or server logs. It tracks request rates from IPs, compares against baselines, and detects anomalies based on volume or velocity. If a DDoS pattern is detected, automated defense mechanisms are triggered to block or throttle malicious requests.

  • Monitor traffic flow using NetFlow, iptables logs, or API gateway logs.
  • Define safe thresholds or use AI/ML to detect volume anomalies and bursts.
  • Log suspected malicious IPs and classify them as potential botnets or compromised hosts.
  • Implement mitigation via firewall rules, reverse proxy filters, or cloud-based defenses.
  • Send alerts to administrators for manual or automated intervention.
Recommended Tech Stack

Traffic Monitoring

tcpdump, Wireshark, or iptables log parser for packet-level traffic monitoring.

Anomaly Detection

Python (scikit-learn or IsolationForest), or rule-based filters using Suricata/Snort.

Backend & Dashboard

Flask or Django API with React/Chart.js dashboard for live metrics and control panel.

Mitigation Layer

IPTables, HAProxy, Fail2Ban, or integration with Cloudflare/WAF APIs.

Step-by-Step Build Plan

1. Capture and Log Incoming Traffic

Use tools like tcpdump or NetFlow to record traffic volume and IP data in real time.

2. Implement Detection Rules or AI Model

Create threshold rules or use ML-based anomaly detection to flag suspicious patterns.

3. Create Mitigation Engine

Build logic to block IPs, rate-limit traffic, or reroute suspected attacks.

4. Design Visualization Dashboard

Display traffic graphs, DDoS alerts, blacklisted IPs, and system status.

5. Add Alerting and Auto-Response

Trigger notifications and optionally automate firewall changes or scaling responses.

Helpful Resources for Development

Defend Your Systems Against DDoS Attacks

Build a smart, responsive system to detect and prevent distributed denial of service attacks — protect your uptime and reputation.

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