AI/ML Security Research

Pixrei

Building intelligent systems at the intersection of Machine Learning and Cybersecurity

Blog

About

Computer Science Student | AI/ML Researcher | Security Practitioner

Building intelligent systems at the intersection of Machine Learning and Cybersecurity. I'm a 2nd-year Computer Science student with hands-on expertise in both domains, focusing on practical solutions that detect, prevent, and respond to security threats using advanced ML techniques.

My approach combines rigorous penetration testing experience (25+ CTF boxes, 3+ security certifications) with deep learning research. I'm currently following an intensive 9-month ML mastery roadmap, progressing from fundamentals through production deployment, with a specialized focus on security applications.

I believe the future of cybersecurity lies in adaptive, intelligent systems—not static rules. Every line of code and every model I build is designed to create real-world impact.

🎓 CS Student (2nd Year)
📊 ML Researcher
🔒 Security Practitioner
🛠 Linux/Arch Expert
📈 42+ Days ML Learning
🎯 2 Deployed Products
25+
CTF Boxes Completed
2
ML Products Deployed
5
Active Projects
3
Security Courses

Featured Projects

ML + Security in Action

🚀 In Progress
🎯

Network Intrusion Detection

Building a Flask-based ML system for real-time network anomaly detection. Developing ensemble models using Scikit-learn to identify suspicious patterns in network traffic. Focus: accuracy, latency optimization, and practical deployment architecture.

Status: Model training phase | Expected: Scikit-learn phase completion
Flask Scikit-learn Network Analysis Python
🚀 In Progress
🔍

Phishing Detection System

Developing an intelligent phishing detection system combining NLP and ML. Designing models to identify malicious URLs, phishing emails, and credential harvesting attempts. Targeting lightweight architecture for browser extension or API deployment.

Status: Feature engineering phase | Expected: Deep learning phase (Month 2-3)
NLP TensorFlow JavaScript Security
📋 Planning
📊

Advanced Security ML Research

Planned research into adversarial robustness and concept drift in security ML models. Investigating production deployment challenges, model interpretability, and adaptive learning systems for real-world threat detection scenarios.

Status: Research phase | Expected: Month 4-5 (post-Deep Learning)
Research TensorFlow/PyTorch Adversarial ML Production ML

Skills & Expertise

Tools and technologies I work with

Machine Learning

  • NumPy & Pandas
  • Scikit-learn
  • TensorFlow / PyTorch
  • Model Deployment
  • Feature Engineering

Cybersecurity

  • Penetration Testing
  • Network Analysis
  • Security Research
  • Threat Detection
  • System Hardening

Programming

  • Python (Advanced)
  • JavaScript / HTML / CSS
  • Flask / FastAPI
  • Linux (Arch, CentOS)
  • Docker & Deployment

Development Workspace

pixrei@ml-researcher ~/projects
$ python train_model.py --phase ml-fundamentals
Initializing ML Fundamentals Phase...
Loading NumPy & Pandas frameworks... ✓
Current Week: 1-2 | Focus: Array operations, DataFrames
Training Progress:
→ Next Phase: Scikit-learn & First Projects
$
🚀 In Progress

9-Month ML Mastery Roadmap

Intensive learning path combining ML fundamentals with security specialization. Structured progression from theory to production-ready systems.

Phase 1 (Month 1/9) | Current: NumPy & Pandas (Week 1-2)

📊 Phase 1
ML Fundamentals
🧠 Phase 2-3
Deep Learning
🔒 Phase 4-5
Security ML
🚀 Phase 6-7
Deployment
🎯 Phase 8-9
Polish & Launch

Learning Focus Areas:

  • ✓ ML Foundations: NumPy, Pandas, Scikit-learn, Feature Engineering
  • ✓ Deep Learning: TensorFlow, PyTorch, CNN, RNN, Transformers
  • ✓ Security Applications: Malware detection, Intrusion detection, Adversarial ML
  • ✓ Production Systems: Flask, Docker, Model deployment, API design