Building a Python Packet Sniffer with Quarantine & Geolocation Logic

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Why I’m Building This

As part of my cybersecurity learning journey, I wanted to go beyond passive packet inspection and simulate how a SOC analyst might respond to suspicious traffic. This packet sniffer project is designed to do more than just capture packets—it aims to detect anomalies, tag geolocation, and quarantine malicious traffic based on real-world CVE patterns.

It’s still a work in progress, but I’m documenting the process to share my thinking, challenges, and implementation steps as I go.

Tools & Technologies

  • Python: Core scripting language
  • Scapy: For packet crafting and sniffing
  • GeoIP / IPinfo APIs: For geolocation tagging
  • Custom Quarantine Logic: Inspired by CVE behavior
  • Structured Logging: To simulate SOC workflows

Project Goals

  • Capture live packets and inspect headers (IP, TCP, UDP, etc.)
  • Identify suspicious patterns (e.g. malformed packets, known ports, flagged IPs)
  • Tag packets with geolocation data for context
  • Quarantine or log flagged packets with structured metadata
  • Simulate incident response logic based on CVE examples

Current Architecture (WIP)

from scapy.all import sniff, IP, TCP
import logging

logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)

handler = logging.FileHandler("test.log")
formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
handler.setFormatter(formatter)

logger.addHandler(handler)

logger.info("This is a test of the logger")

def packet_callback(packet):
    if IP in packet:
        src_ip = packet[IP].src
        dst_ip = packet[IP].dst
        # Placeholder for geolocation and quarantine logic
        logging.info(f"Packet from {src_ip} to {dst_ip}")

sniff(prn=packet_callback, store=0)

This is just the skeleton. I’m working on:

  • Integrating geolocation APIs
  • Adding CVE-inspired detection logic
  • Structuring logs with timestamps, severity levels, and packet metadata
  • Creating a quarantine mechanism (e.g. tagging, isolating, or alerting)

Learning Highlights So Far

Scapy’s flexibility makes it ideal for crafting and dissecting packets

Python logging is more powerful than I expected—especially for SOC-style workflows

Geolocation adds context that’s often missing in raw packet data

CVE research helps translate abstract vulnerabilities into practical detection logic

What’s Next

  • Finalize geolocation tagging and test with known IPs
  • Build a rule engine for quarantine decisions
  • Simulate a mini incident report based on flagged traffic
  • Document the full workflow with screenshots and code snippets
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