INTERNET SECURITY · APPLIED ML

BERLIN CET

Researching
the Internet.
Building systems
that protect it.

I am Marcin Nawrocki Freiherr von Enzberg. I combine Internet measurement, DDoS security, and applied machine learning to understand threats and build systems that protect critical networks.

At NETSCOUT, I develop ML-based DDoS detection for core and transit networks, informed by threat intelligence covering roughly half of global Internet traffic.

01 / WORK AREAS

APPLIED ML · DDoS DEFENCE

DDoS detection.
Built for the real world.

I develop statistical and machine-learning detection with production engineering teams. This research has led to multiple patent applications.

INTERNET RESEARCH · RESILIENCE

Internet measurements.
Assumptions, validated.

I study attacks, protocols, and protective mechanisms in deployed networks. I publish and review research at leading Internet measurement and security conferences.

02 / SELECTED PUBLICATIONS

Evidence worth
building on.

Google Scholar ↗
01 ACM IMC · 2024 An Empirical Comparison of Industry and Academic DDoS Assessments

Bringing industry and academic observations together to examine what different vantage points can tell us about DDoS trends.

Read full paper ↗
02 ACM CoNEXT · 2022 · Best Paper & Community Awards On the Interplay between TLS Certificates and QUIC Performance

Why certificate deployment choices can undermine QUIC’s promise of fast, secure connections; and what operators can change.

Read full paper ↗
03 ACM IMC · 2021 Tracing the DDoS Attack Ecosystem from the Internet Core

Combining Internet exchange and honeypot observations to map DNS amplification attacks, identify blind spots and reveal how attackers exploit reflection infrastructure.

Read full paper ↗
04 ACM IMC · 2019 Dismantling Operational Practices of BGP Blackholing at IXPs

Testing whether a widely deployed DDoS mitigation mechanism behaves as expected in an operational Internet exchange.

Read full paper ↗

What is the full picture,
and where are its blind spots?

Which decisions does
the evidence justify?

How do we turn findings
into proven protection?

03 / MISSION & BACKGROUND

A researcher’s curiosity.
An engineer’s responsibility.

Mission

I want an Internet that stays resilient, open and decentralized, where critical infrastructure remains accessible and no single party has to control it all.

Purpose

I turn complex Internet data into trustworthy, actionable evidence. I believe protection should be measurable, decisions should be explainable, and failed assumptions should lead to better systems.

For me, integrity means validating what works, describing the limits and changing course when the evidence demands it.

Selected milestones

  1. NOW · NETSCOUT ASERT

    Turning research into operational detection

    Applied ML and threat intelligence, developed alongside Arbor Sightline engineering teams.

  2. 2024 · FREIE UNIVERSITÄT BERLIN

    Doctorate, summa cum laude

    DDoS Attacks: Coverage, Mitigation, and Prevention. Supervised by Matthias Wählisch; assessed by Thomas C. Schmidt and KC Claffy.

  3. 2022 · ACM CONEXT

    Best Paper & Community Awards

    Research on TLS certificate choices and QUIC performance.

  4. ONGOING

    Research community & mentoring

    Reviewer for top-tier conferences, including ACM IMC and ACM CCS.

Full CV (PDF) ↗

04 / DOMAINS & tools.

Internet & protocols

  • Internet measurement & telemetry
  • Internet core, IXPs & transit
  • BGP routing & mitigation
  • DNS & amplification
  • QUIC, TLS & performance
  • IPv6 & anycast

Threats & protection

  • DDoS detection & mitigation
  • Threat intelligence
  • Attack ecosystems & scanning
  • Honeypots & network telescopes
  • Coverage & blind spots
  • Protection effectiveness & resilience

Statistical methods & ML

  • Robust statistics & quantiles
  • Anomaly detection & ML
  • Signal fusion
  • Feature engineering & time series
  • Model validation & drift
  • Explainable detection

Engineering & evaluation

  • Python, Polars & NumPy
  • SQL, Athena & ClickHouse
  • Parquet data pipelines
  • PyTorch & scikit-learn
  • MLOps & MLflow
  • Evaluation & reproducibility
  • Production ML systems

LET’S CONNECT

Working on a hard
Internet problem?

Get in touch