DEV Community: Gulrez Qayyum

Building an AI-Powered Network Traffic Analyzer with FastAPI and Scapy (Looking for Contributors)

Gulrez Qayyum — Fri, 17 Jul 2026 12:11:12 +0000

Over the past few weeks I've been building NetGuard, an open-source AI-powered Network Traffic Analyzer and Active Defense Platform.

The goal is to combine traditional intrusion detection with machine learning into a single platform capable of:

Capturing live packets using Scapy
Detecting suspicious traffic using IDS rules
Classifying traffic with a Random Forest model
Automatically blocking malicious IPs
Providing remediation recommendations
Generating incident reports

Most of the system is already working, including packet capture, IDS detection, remediation, dashboard integration, and IP blocking.

The remaining work is mainly around integration.

The most interesting bug is that the FastAPI orchestrator successfully receives HTTP 200 OK from the classifier service, yet ai_result is always returned as null.

I've investigated payload formatting, response parsing, and model loading, but I'm convinced another pair of eyes could spot what I'm missing.

I've documented the architecture, setup instructions, debugging notes, and open issues in the repository.

If you're interested in Python, FastAPI, networking, cybersecurity, or machine learning, I'd love your feedback or contributions.

Repository:
https://github.com/GulrezQayyum/netguard

Open-source is one of the best ways to learn, so even documentation improvements or code reviews are welcome.

Apex F1 – The Ultimate Formula 1 Racing Simulator

Gulrez Qayyum — Mon, 13 Jul 2026 12:51:08 +0000

This is a submission for Weekend Challenge: Passion Edition

Demo

https://apex-f1-159.web.app

Code

https://github.com/GulrezQayyum/apex-f1

Inspiration

Formula 1 has always fascinated me because it's not just about speed—it's a sport driven by strategy, teamwork, weather, tyre management, and split-second decisions. I wanted to capture those elements in a mobile app rather than building a traditional arcade racing game. Apex F1 is my attempt to recreate the excitement of managing a race weekend while combining my passion for Formula 1 with Flutter development.

How I Built It

I built Apex F1 entirely with Flutter, focusing on creating a realistic Formula 1 racing and strategy experience for mobile devices. As a huge Formula 1 fan, I wanted to go beyond a simple racing game and recreate the tactical decisions that make the sport exciting.

The project started with designing the app's architecture and user interface in Flutter. I then developed the race simulation engine, which powers every lap of the race. The simulator considers factors such as tyre degradation, fuel strategy, weather conditions, pit stops, safety cars, and AI driver behavior to produce dynamic race outcomes.

To make each race feel unique, I implemented:

GPS-inspired circuit layouts based on real Formula 1 tracks.
Twenty AI-controlled drivers with different performance characteristics.
Dynamic weather that can change race strategy.
Tyre wear and pit stop strategy affecting lap times.
Championship standings that carry progress across an entire season.
Multiple seasons (2023–2025) with updated teams and drivers.

The UI was designed to be clean and immersive, allowing users to follow race events, monitor strategy, and track championship progress without feeling overwhelmed.

The biggest challenge was balancing realism with performance. Building a simulation that remained responsive on mobile devices required careful optimization and repeated testing. I refined the race logic over many iterations until the races felt both competitive and unpredictable.

Overall, Apex F1 combines my interests in Flutter development, simulation programming, and Formula 1 into a single project that reflects both my technical skills and my passion for the sport.

Prize Categories

Best Use of Google AI

Apex F1 integrates Google Gemini AI to enhance the racing experience beyond traditional simulation. Google AI powers intelligent race insights and assists users with strategy recommendations, making the app more interactive and engaging. By combining AI-driven assistance with a realistic Formula 1 race simulator built in Flutter, Apex F1 delivers a smarter and more immersive experience for F1 fans.

Built by: Gulrez Qayyum — an indie developer passionate about Flutter, AI, and Formula 1.

Built a Network Traffic Classifier with Random Forest (96.8% Accuracy)

Gulrez Qayyum — Sun, 17 May 2026 18:47:18 +0000

I recently completed a cybersecurity + machine learning project where I trained a Random Forest model to classify network traffic into multiple attack categories using the NSL-KDD dataset.

The classifier can detect:

DoS attacks
Probe/reconnaissance traffic
R2L brute-force attempts
U2R privilege escalation
Normal traffic
Stack
Python
Scikit-learn
FastAPI
Pandas / NumPy
Results
96.8% Accuracy
<1ms inference time
Production-ready model packaging

I also wrote a detailed Medium article covering:

Dataset preprocessing
Feature selection
Model training
API integration
Real-world deployment considerations
Challenges working with synthetic vs real traffic data

Would appreciate feedback from the community, especially from people working in:

Cybersecurity
ML Engineering
Intrusion Detection
Backend Systems

Article: https://medium.com/@gulrezqayyum/building-a-production-ready-network-traffic-classifier-with-random-forest-and-nsl-kdd-b9559346e197

GitHub:
https://github.com/GulrezQayyum

LinkedIn:
https://www.linkedin.com/in/gulrez-qayyum-999345322/