ROADMAP MENJADI MACHINE LEARNING CYBER SECURITY ENGINEER
✅ PHASE 1 — FUNDAMENTAL SKILL (3–6 bulan)
1️⃣ Dasar Pemrograman & Data Science
Tujuan: memahami coding + data preprocessing
Skill Wajib:
- Python (NumPy, Pandas)
- Exploratory Data Analysis (EDA)
- Visualization (Matplotlib, Seaborn)
- Basic statistics
Target Output:
✔ Python automation untuk parsing log
✔ Data cleaning untuk log keamanan (syslog, firewall log, PLC logs)
2️⃣ Fundamental Cyber Security
Tujuan: memahami domain ancaman
Materi Penting:
- CIA Triad
- Network Security, Firewall, IDS/IPS
- Malware, phishing, brute force, DDoS
- Basic SIEM (ELK, Splunk)
Target Output:
✔ Mampu membaca traffic Wireshark
✔ Paham log: auth.log, netflow, Modbus traffic
✅ PHASE 2 — ML CORE SKILLS (3–6 bulan)
3️⃣ Machine Learning Untuk Keamanan
Materi:
- Supervised: Decision Tree, Random Forest, SVM, XGBoost
- Unsupervised: Clustering, PCA, Isolation Forest
- Evaluation metrics keamanan:
- Precision/Recall
- ROC-AUC
- False Positive Rate
Target Output:
✔ Model deteksi brute force login
✔ Model deteksi DDoS (dataset CIC-IDS, UNSW-NB15)
4️⃣ Deep Learning for Cybersecurity
Skill:
- ANN
- LSTM untuk anomaly detection
- CNN untuk malware classification (binary images)
- Autoencoder untuk deteksi intrusi
Target Output:
✔ Autoencoder untuk deteksi anomali traffic
✔ LSTM untuk memprediksi serangan ICS/IIoT
✅ PHASE 3 — CYBERSECURITY SPECIALIZATION (6–12 bulan)
5️⃣ Network Traffic Analysis + AI
Belajar:
- Netflow
- PCAP preprocessing
- Feature extraction otomatis (CICFlowMeter)
Project:
✔ “AI-Based Intrusion Detection System for ICS/SCADA”
✔ “Anomaly Detection di PLC Traffic (Modbus/TCP)”
6️⃣ ML for ICS/SCADA Security (Khusus Industri)
Fokus:
- Protocol: Modbus, DNP3, OPC-UA, Profinet
- ICS threat (unauthorized write, parameter tampering)
- Creating digital twins for anomaly detection
- Hardening PLC network
Project:
✔ Deteksi anomali Modbus function code
✔ ML mendeteksi perubahan parameter PLC (speed, level, temp)
✔ IDS khusus IIoT berbasis ESP32 + ML
7️⃣ Adversarial Machine Learning
Materi:
- Evasion attack (mengelabui model IDS)
- Poisoning attack (meracuni dataset)
- Model robustness
- AI malware generator vs AI malware detector
Project:
✔ Sistem IDS tahan serangan adversarial
✔ Detect fake IoT traffic generated by GAN
✅ PHASE 4 — SECURITY ENGINEERING + DEPLOYMENT
8️⃣ MLOps + Deployment for Security
Skill:
- Docker, Kubernetes
- REST API (FastAPI)
- Model optimization
- Edge AI (Jetson Nano, Raspberry Pi, ESP32-S3)
Project:
✔ Deploy ML IDS ke Raspberry Pi sebagai IoT Gateway
✔ Deteksi serangan Modbus real-time
9️⃣ SIEM + SOC Automation with AI
Belajar:
- ELK untuk log ingestion
- Correlation rules + ML engine
- SOAR automation
Project:
✔ AI yang otomatis mengirim alert ke Telegram
✔ AI-based anomaly scoring in SIEM dashboard
✅ PHASE 5 — ADVANCED (6–12 bulan)
🔟 Blockchain for Cyber Security (opsional tapi kuat)
- IoT identity management
- Immutable logging
- Smart contract untuk “security policy enforcement”
Project:
✔ Ethereum / Polygon untuk secure IoT device identity
✔ Blockchain untuk log yang tidak bisa dihapus (tamper-proof)
🎯 ALUR ROADMAP VISUAL
1 → 2 → 3 → 4 → 5 → 6 → 7 → 8 → 9 → 10
Fundamental → ML → Cybersecurity → ICS → Deployment → Blockchain
🧪 PROJECT CAPSTONE (Wajib untuk Portofolio)
1️⃣ AI-Based IDS for IIoT (Modbus/RTU & TCP)
- Menggunakan LSTM/Autoencoder
- Traffic real Modbus PLC
- Deploy di Raspberry Pi
2️⃣ Anomaly Detection untuk PLC/SCADA
- Monitoring: level, flow, temp, pressure
- Detect out-of-bound changes
3️⃣ AI-Enabled SOC Dashboard
- Menghubungkan SIEM
- ML classifier: brute force login / malware
4️⃣ Blockchain Identity for IoT Devices
