🛰️ Analyzing Network Traffic with Moloch and Elastic

📖 Introduction

While diving deeper into the realm of Cybersecurity, I decided to explore the inner workings of network traffic and how anomalies can be detected in real-time. This project titled "Analyzing Network Traffic with Moloch and Elastic" became a stepping stone for me to blend packet inspection, data indexing, and visualization — all in one flow.

The core goal was to integrate Moloch (now known as Arkime) with the Elastic Stack to build a robust network monitoring setup. This hands-on implementation sharpened my understanding of how professionals detect suspicious behavior, analyze performance bottlenecks, and draw insights from raw PCAP files.

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🧰 Tools & Technologies Used

• 🧠 Moloch (Arkime) — Full packet capture and indexing system.
• 🧱 Elasticsearch — Distributed search and analytics engine.
• 📊 Kibana — Visualization layer for traffic patterns.
• 🔁 Logstash — Optional for ingest pipelines and filtering.
• 🧪 Wireshark — For deep packet inspection and PCAP parsing.
• 📂 PCAP files — Captured network traffic data.
• 🛡️ SIEM Integration (Optional) — For correlation with broader security systems.
• 🐧 Ubuntu — Deployment and testing environment.

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⚙️ Project Workflow

1. 📥 Moloch Setup & Configuration

   • Installed Arkime/Moloch on Ubuntu.
   • Configured interface to capture traffic (e.g., eth0).
   • Set up PCAP storage, viewer, and capture service.

2. 📡 Capturing Network Traffic

   • Captured live traffic using Moloch or imported .pcap files via moloch-capture.
   • Verified session data in Moloch Viewer UI.

3. 🔍 Indexing & Storage (Elastic Integration)

   • Moloch pushed metadata and indexes to Elasticsearch.
   • Defined index patterns in Kibana for querying sessions.

4. 📊 Visualization & Dashboarding

   • Built dashboards in Kibana to view:
     • Top IP talkers
     • Anomalous port usage
     • Protocol-specific analysis (HTTP, DNS, etc.)

5. 🧠 Traffic Analysis

   • Identified:
     • Suspicious activities (port scans, high data transfers)
     • Performance issues (packet loss, high RTT)
     • Protocol-level misuse

6. ✅ Final Audit

   • Shared improvement suggestions for securing the network perimeter based on insights.

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🗂️ Repository Structure

📁 Total Files: 7

• 📄 Project Report PDF — Concise Step-by-step Explaination of how project is performed with Conclusion.
• 📄 Project PPT — Contains basic information about the project.
• 📄 screenshots — Visual outputs from Kibana and Moloch Viewer.
• 📄 README.md — You're here.

Each folder contains its own README.md (where required) for context-specific instructions.

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📸 Screenshots

• 🖥️ kibana-dashboard.png — Overview of active sessions and traffic heatmap.
• 🧾 moloch-session-view.png — Detailed PCAP-level session drilldown.
• 🔍 protocol-distribution.png — Traffic by protocol breakdown (HTTP, DNS, TLS).

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🎯 Final Takeaway

This project transformed raw network data into insightful security stories. For the first time, I worked with real traffic datasets to identify potential threats, bottlenecks, and behavioral patterns. It refined my understanding of:

• How packet data is captured and stored at scale.
• How Elastic’s search engine can be tailored for security.
• How visualization plays a key role in interpreting raw traffic.

Most importantly, I now see network analysis as an art — one that sits at the core of Cyber Defense.

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🔗 References / Learning Resources

1. 📘 Moloch Documentation
2. 📘 Elastic Documentation
3. 📘 Wireshark User Guide
4. 📘 Intro to Network Traffic Analysis - Blog
5. 📘 Tutorial: Moloch + Elastic

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