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Niher Halder

Niher Halder

Engineer–Strategist for Critical Infrastructure Risk & Resilience in Power & Industrial Systems

Vision

To strengthen the safety, resilience, and trustworthiness of critical power and industrial systems by bridging real-world engineering experience with explainable, process-aware OT/ICS cybersecurity.

Mission

  • Apply deep understanding of safety-critical infrastructure to identify and mitigate system-level risks in power and industrial environments.
  • Develop and promote process-aware, explainable security approaches that align with how industrial systems actually operate.
  • Integrate risk engineering, resilience thinking, and OT/ICS cybersecurity to support secure and reliable critical energy systems.
  • Share practical insight through research, writing, and engineering-led security practice to contribute to safer critical infrastructure worldwide.

Current focus: AI-driven cybersecurity for industrial systems — integrating automation and data-driven analysis to support anomaly detection and threat modeling in power and energy environments. My long-term direction is to bridge engineering reality, cybersecurity, and intelligent automation for critical infrastructure in ways that remain practical, explainable, and operationally safe.

👋 Welcome to niher.tech — building toward OT/ICS cybersecurity + AI for critical infrastructure.

Welcome to My Work

My website is structured around three pillars: field experience, focused writing, and target-aligned projects.

📷 Field Experience Gallery

A visual record of my work in safety-critical infrastructure—showing the environments where reliability, safety, and operational constraints matter.

Safety-critical On-site reality Engineering rigor
Open Gallery

✍️ OT/ICS Security Blog

Articles on AI-driven cybersecurity in gas & power—anomaly detection, process-aware defense, and practical thinking for operators and engineers.

Gas & Power Anomaly Detection Threat Modeling
Read Blog

🧪 Target-Aligned Projects

A curated set of projects showing strength in AI, programming, automation, and security-thinking—built with clear documentation.

AI/ML Automation Explainability
Explore Projects

Field Experience (Preview)

These photos highlight the environments and constraints that shape my approach to cybersecurity: safety, uptime, process integrity, and real-world tradeoffs.

View Full Gallery

Latest Writing (Preview)

Short, structured posts focused on OT/ICS security + AI for gas and power systems. (Start with 3 posts and grow steadily.)

Why IT Security Models Fail in Power & Gas Plants

What changes when uptime and safety are non-negotiable—and how defenders should adapt.

Category: OT/ICS Fundamentals • 6–8 min read
Anomaly Detection Needs Process Context

Reducing false positives by connecting signals to operations and process states.

Category: AI for OT • 7–10 min read
Threat Modeling in OT: Start With Consequence

Map cyber events to physical impact and prioritize mitigations that won’t disrupt operations.

Category: Threat Modeling • 6–9 min read
See All Posts

Featured Projects (Preview)

A small set of deeply documented projects. Fewer projects, higher quality.

🏠 House Price Prediction (XGBoost)

ML workflow practice: feature engineering, validation, tuning, and deployment mindset.

  • CV RMSE: 0.1229 (log-space)
  • Kaggle Score: 0.12826
  • Status: ✅ Completed

🔗 GitHub Repo  |  📄 Summary PDF

🧠 Facial Keypoints Detection (CNN)

Deep learning practice: architecture, training stability, evaluation discipline.

  • Val RMSE: 0.0230
  • Val MAE: 0.0163
  • Status: ✅ Completed

🔗 GitHub Repo  |  📄 Summary PDF

Next (OT/ICS Project Roadmap)

Planned: OT/ICS anomaly detection (time-series telemetry + operationally meaningful alerts). Will include baselines, model comparison, false-positive control strategy, and an architecture diagram.

Go to Projects