BS Cybersecurity | Sir Syed CASE Institute of Technology, Islamabad Aspiring SOC Analyst | Building AI-powered security tools
Real-time Intrusion Detection & Prevention System
- π― 99.92% accuracy with an ensemble of Random Forest, DNN & SVM
- π‘ Live packet capture with Scapy + auto-blocking IPS (4-tier threat classification)
- π₯οΈ Purple Gold Cyberpunk dashboard with live monitoring
- π bcrypt auth, OTP password reset, PDF reporting, GeoIP tracking
- β OWASP compliant, with auto-installers for Windows & Linux
Tech: Python Β· Flask Β· Scikit-learn Β· TensorFlow Β· Scapy Β· Chart.js
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AI-powered port scanner & network risk assessment platform
- β‘ Async port scanning with real-time risk scoring
- π€ ML-based threat detection (Random Forest / XGBoost trained on NSL-KDD, 99.58% accuracy)
- π§ CVE correlation powered by Groq API (Llama 3.3 70B)
- π Automated PDF report generation
- π€ Dark military terminal aesthetic
Tech: Python Β· Flask Β· asyncio Β· Scikit-learn Β· XGBoost Β· Groq API Β· ReportLab
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ML-powered phishing detection with cloud deployment
- π― ~97% detection accuracy
- βοΈ Live cloud deployment via Railway
- π Real-time URL analysis and risk classification
Tech: Python Β· Machine Learning Β· Railway
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AI-powered resume optimization tool for ATS compatibility
- π€ AI-driven resume scoring & keyword optimization
- π ATS compatibility analysis and actionable feedback
- βοΈ Tailored suggestions to boost interview callbacks
Tech: Python Β· AI/NLP
π View Project
Python Β· Flask Β· Scikit-learn Β· TensorFlow Β· XGBoost Β· Scapy Β· Chart.js Β· Groq API Β· ReportLab