Cybersecurity / Network Security
Network Traffic Analyzer
A cybersecurity tool built with Python, Django, and Scapy that performs live packet capture and protocol analysis — TCP, UDP, ICMP, DNS, and ARP — with traffic statistics, a SOC-style monitoring dashboard, and authenticated REST APIs.
Cybersecurity / Network Security
01 · Overview
The Network Traffic Analyzer is a network security monitoring application. It captures live traffic, analyzes packets at the protocol level (TCP, UDP, ICMP, DNS, ARP), and presents the results through a SOC-style dashboard with traffic statistics and passive anomaly indicators. All data access is through authenticated REST APIs.
02 · Problem
Network activity is invisible without tooling. Security teams and learners need a way to observe what is actually traversing a network — which protocols dominate, where anomalies appear, and which hosts are communicating — without relying on opaque commercial appliances.
03 · Solution
The analyzer wraps Scapy-based packet capture in a Django application. Captured packets are normalized, analyzed per protocol, and stored so they can be explored and reported on. A SOC-style dashboard visualizes live statistics and flags passive anomalies, while an authenticated REST API exposes the same data programmatically.
04 · Architecture
Python and Django form the application core. Scapy performs packet capture and dissection; analyzed frames are persisted to a database for history, statistics, and reporting. The frontend dashboard consumes the Django REST API. The full application is containerized with Docker for reproducible deployments on Linux.
05 · Key Features
- Live packet capture and dissection
- Protocol-level analysis: TCP, UDP, ICMP, DNS, ARP
- Traffic statistics and host communication summaries
- SOC-style monitoring dashboard
- Passive anomaly indicators (no active intrusion attempts)
- Authenticated REST APIs for programmatic access
- Packet exploration and exportable reporting
07 · Challenges
- Normalizing captured frames across heterogeneous protocols into a consistent data model.
- Processing high-volume packet streams without dropping data or blocking the application.
- Separating meaningful anomaly signals from normal network noise.
09 · Outcome
A functioning network monitoring system that demonstrates cybersecurity engineering: packet-level understanding, security monitoring, Python development, and authenticated backend APIs. It is the flagship evidence of the cybersecurity specialization.