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Prof. Dr. Konrad Rieck


Technische Universität Berlin
Machine Learning and Security

Ernst-Reuter-Platz 7, 10587 Berlin

Rieck Konrad BIFOLD Research Security Machine Learning
© TU Braunschweig

Prof. Dr. Konrad Rieck

Research Group Lead

Konrad Rieck is a professor at TU Berlin, where he leads the Chair of Machine Learning and Security as part of the Berlin Institute for the Foundations of Learning and Data (BIFOLD). Previously, he held academic positions at TU Braunschweig, the University of Göttingen, and Fraunhofer Institute FIRST. His research focuses on the intersection of computer security and machine learning. He has published over 100 papers in this area and serves on the PCs of the top security conferences (aka the system security circus).

Recent Project

  • ERC Consolidator Grant: "MALFOY: Machine Learning for Offensive Computer Security"
  • Excellence Cluster CASA: "TELLY: Testing the Limits of Machine Learning in Vulnerability Discovery
  • Excellent Cluster CASA: "PACO: Analysis and Discovery of Parser-Confusion Vulnerabilities"
  • DFG Project: "ALISON: Attacks against Machine Learning in Structured Domains"

  • USENIX Security Distinguished Paper Award 2022
  • ERC Consolidator Grant 2021
  • AISEC Best Paper Award 2021
  • Winner of Microsoft MLSEC competition 2020
  • LehrLeo: Best Lecture at TU Braunschweig 2019
  • LehrLeo: Best Lab Course at TU Braunschweig 2019
  • German Prize for IT-Security 2016 (2nd Place)
  • DIMVA Best Paper Award 2016
  • Google Faculty Research Award 2014
  • ACSAC Outstanding Paper Award 2012
  • CAST/GI Dissertation Award IT-Security 2010

  • Intelligent Security Systems
  • Attack Detection and Prevention
  • Malware and Vulnerability Analysis
  • Adversarial Machine Learning

  • BMBF Plattform Lernende Systeme
  • European Laboratory for Learning and Intelligent Systems (ELLIS)
  • Gesellschafft für Informatik
  • Forum InformatikerInnen für Frieden und gesellschaftliche Verantwortung

Thorsten Eisenhofer, Erwin Quiring, Jonas Möller, Doreen Riepel, Thorsten Holz, Konrad Rieck

No more Reviewer #2: Subverting Automatic Paper-Reviewer Assignment using Adversarial Learning

August 09, 2023

Alexander Warnecke, Lukas Pirch, Christian Wressnegger, Konrad Rieck

Machine Unlearning of Features and Labels

February 27, 2023

Daniel Arp, Erwin Quiring, Feargus Pendlebury, Alexander Warnecke, Fabio Pierazzi, Christian Wressnegger, Lorenzo Cavallaro, Konrad Rieck

Dos and Don’ts of Machine Learning in Computer Security

August 10, 2022

Alexander Warnecke, Daniel Arp, Christian Wressnegger, Konrad Rieck

Evaluating Explanation Methods for Deep Learning in Security

September 07, 2020

Cyber Security| Jul 01, 2024

BIFOLD researchers present four papers at ASIACCS 2024

The 19th ACM ASIA Conference on Computer and Communications Security (ASIACCS 2024) will take place in Singapore from July 1 to July 5, 2024. The conference will focus on specific areas of computer science, such as information security and information privacy.

Machine Learning| May 22, 2024

IEEE Test-of-Time Award for Konrad Rieck

Together with his former PhD student Fabian Yamaguchi and the entire team, Konrad Rieck won the Test-of-Time Award at the IEEE Symposium on Security and Privacy for their paper: "Modeling and Discovering Vulnerabilities with Code Property Graphs ".  The IEEE Symposium on Security and Privacy is the oldest and most important conference in computer security. Each year, the "Test-of-Time Award" is given to papers that significantly influenced research and can be described as groundbreaking.

Machine Learning| May 12, 2024

Researcher Spotlight: Dr. Alexander Warnecke

Congratulations to Dr. Alexander Warnecke who succesfully defended his PhD "Security Viewpoints on Explainable Machine Learning" on April 16, 2024.

Cyber Security| Mar 11, 2024

Project Launch AIgenCY

With the innovative research project "AIgenCY - Opportunities and Risks of Generative AI in Cybersecurity," leading experts from academia and industry take on the challenge of exploring the implications of generative artificial intelligence (AI) for cybersecurity.

Cyber Security| Oct 25, 2023

Email security under scrutiny: Examining SPF weaknesses

Cyber security researchers at BIFOLD found weaknesses in Sender Policy Framework (SPF) records, which protect email users from forged senders. The paper “Lazy Gatekeepers: A Large-Scale Study on SPF Configuration in the Wild” includes an analysis of 12 million domains’ SPF records and was now presented at the 2023 ACM Internet Measurement Conference.

| Aug 14, 2023

Adversarial Papers: New attack fools AI-supported text analysis

Security researchers have found significant vulnerabilities in learning algorithms used for text analysis: With the help of a novel attack, they were able to show that topic recognition algorithms can be fooled by even small changes in words, sentences and references. 

BIFOLD Update| Jul 10, 2023

BIFOLD welcomes Israel delegation

Among other institutions the BIFOLD hosted a delegation from Israeli universities as part of Germany's "Willkommen" Visitors Programme. Various BIFOLD researchers gave a short introduction to their research foci in AI. The “Willkommen” programme invites opinion leaders to experience Germany and gain a nuanced understanding of the country.

| Jan 02, 2023

Machine Learning and Security

Welcome to Prof. Dr. Konrad Rieck, who heads the new workgroup Machine Learning and Security at  BIFOLD and TU Berlin and started January 1, 2023.

| Nov 02, 2022

Cybersecurity under scrutiny

In cybersecurity research, machine learning (ML) has emerged as one of the most important tools for investigating security-related problems: However, a group of European researchers from TU Berlin, TU Braunschweig, University College London, King’s College London, Royal Holloway University of London, and Karlsruhe Institute of Technology (KIT)/KASTEL Security Research Labs, led by BIFOLD researchers from TU Berlin, have shown recently that research with ML in cybersecurity contexts is often prone to error.

| Oct 07, 2021

Preventing Image-Scaling attacks on Machine Learning

BIFOLD Fellow Prof. Dr. Konrad Rieck, head of the Institute of System Security at TU Braunschweig, and his colleagues provide the first comprehensive analysis of image-scaling attacks on machine learning, including a root-cause analysis and effective defenses. Konrad Rieck and his team could show that attacks on scaling algorithms like those used in pre-processing for machine learning (ML) can manipulate images unnoticeably, change their content after downscaling and create unexpected and arbitrary image outputs. The work was presented at the USENIX Security Symposium 2020.