I'm an AI/ML engineer and Communications & Electronics researcher building at the intersection of artificial intelligence and cybersecurity. My work spans edge devices, wireless security systems, indoor positioning, and SOC automation.
- Published researcher — Real-time Detection of Wi-Fi Attacks using Hybrid Deep Learning on NodeMCU · Scientific Reports (Nature), 2025 · 5,900+ reads
- Teaching Assistant in AI/ML — Misr University of Science & Technology (MUST)
- Builder of production-representative security labs: Snort + Wazuh + SOAR pipelines
- Focused on: Edge AI · IoT Security · ML for Networking · SOC Automation
| Domain | Technologies |
|---|---|
| ML / Deep Learning | TensorFlow, Scikit-learn, LSTM, GRU, RNN, SVM, KNN, MLP |
| Security Tools | Wazuh, Snort 3, T-Pot, Shuffle SOAR, Kali Linux, Sysmon |
| Languages | Python, C, Shell/Bash |
| APIs & Deployment | Flask, REST APIs, Jupyter Notebooks |
| Embedded / IoT | NodeMCU, Arduino, ESP8266 |
| Frameworks / Standards | NIST CSF 2.0, MITRE ATT&CK |
| Project | Description | Stack | Status |
|---|---|---|---|
| SOC Monitoring Lab | Full SOC mini-lab: Snort IDS/IPS + Wazuh SIEM + T-Pot honeypot + Shuffle SOAR. 5–15s detection-to-block. | Security, Python | ✅ Live |
| WiFi Attack Detection | Edge AI system detecting Wi-Fi deauth attacks in real-time on NodeMCU — 96% accuracy. Nature paper. | TF, LSTM, IoT | ✅ Published |
| RSSI Indoor Localization | ML system predicting indoor position from WiFi RSSI fingerprints. KNN/SVM/MLP + Flask REST API. | Python, sklearn | ✅ Live |
| Student Grade Analyzer | Modular Python tool for academic performance analytics with visual dashboard and reports. | Python, pandas | ✅ Live |
Real-time Detection of Wi-Fi Attacks using Hybrid Deep Learning on NodeMCU
Scientific Reports — Nature Portfolio, 2025
Hybrid LSTM/GRU/RNN model deployed on a NodeMCU edge device achieving 96% accuracy on real-time deauthentication attack detection.
5,900+ reads since publication.
📄 Read the paper
Languages
AI / Machine Learning
Security & Infrastructure
- Hybrid Deep Learning for edge devices (published)
- SOC automation with open-source tools
- ML-based indoor positioning systems
- LLM-powered security assistants
- ML on network traffic datasets (CICIDS, NSL-KDD)
- Google Professional ML Engineer certification
I'm open to AI/ML research collaborations and engineering roles in AI, security, or applied ML.