PCAP Traffic Analysis & Threat Detection (Python)

Network Traffic Forensics & Threat Detection

Inspiration I was always curious about what actually happens behind the scenes when devices communicate over a network. With so many smart devices today, I wanted to understand how data flows and how hidden threats can exist inside normal-looking traffic. This project started as an attempt to explore raw network packets and see if I could make sense of them.

How I Built It I worked with PCAP (packet capture) files and used Wireshark to inspect the traffic. Then I used Python to parse and filter useful information like IP addresses, protocols, and sessions. By organizing packets based on timestamps, I was able to rebuild activity timelines and understand how devices were communicating over time.

What I Learned

• How network protocols like TCP, HTTP, and DNS actually work
• How to read and analyze raw packet data
• The difference between normal traffic and suspicious behavior
• How small patterns in data can reveal bigger insights

Challenges I Faced

One of the biggest challenges was dealing with messy and unstructured data. PCAP files contain a lot of noise, so filtering useful information took time.

Another challenge was reconstructing timelines because packets are not always in order. It was also difficult to confidently identify what counts as a real threat versus normal activity.

Outcome In the end, I was able to convert raw packet data into meaningful insights and understand network behavior better. This project gave me a strong foundation in traffic analysis and showed how low-level data can be used for security purposes.

Decode Traffic. Detect Threats.Inspiration

What it does

How I built it

Challenges I ran into

Accomplishments that I'm proud of

What I learned

What's next for PCAP Traffic Analysis & Threat Detection (Python)

Built With

• aws-(ec2
• express.js
• firebasemongodb
• javascript-(es6+)
• node.js
• python
• react.js
• rest-apis
• s3-basics)
• websockets