Search Results for author: Konrad Rieck

Found 20 papers, 11 papers with code

Manipulating Feature Visualizations with Gradient Slingshots

1 code implementation11 Jan 2024 Dilyara Bareeva, Marina M. -C. Höhne, Alexander Warnecke, Lukas Pirch, Klaus-Robert Müller, Konrad Rieck, Kirill Bykov

Deep Neural Networks (DNNs) are capable of learning complex and versatile representations, however, the semantic nature of the learned concepts remains unknown.

Decision Making

On the Detection of Image-Scaling Attacks in Machine Learning

1 code implementation23 Oct 2023 Erwin Quiring, Andreas Müller, Konrad Rieck

Unfortunately, this preprocessing step is vulnerable to so-called image-scaling attacks where an attacker makes unnoticeable changes to an image so that it becomes a new image after scaling.

Learning Type Inference for Enhanced Dataflow Analysis

1 code implementation1 Oct 2023 Lukas Seidel, Sedick David Baker Effendi, Xavier Pinho, Konrad Rieck, Brink van der Merwe, Fabian Yamaguchi

Comparing our approach against recent neural type inference systems, our model outperforms the current state-of-the-art by 7. 85% on the ManyTypes4TypeScript benchmark, achieving 71. 27% accuracy overall.

Type prediction

Evil from Within: Machine Learning Backdoors through Hardware Trojans

no code implementations17 Apr 2023 Alexander Warnecke, Julian Speith, Jan-Niklas Möller, Konrad Rieck, Christof Paar

In this paper, we challenge this assumption and introduce a backdoor attack that completely resides within a common hardware accelerator for machine learning.

Backdoor Attack Self-Driving Cars +1

I still know it's you! On Challenges in Anonymizing Source Code

1 code implementation26 Aug 2022 Micha Horlboge, Erwin Quiring, Roland Meyer, Konrad Rieck

We prove that the task of generating a $k$-anonymous program -- a program that cannot be attributed to one of $k$ authors -- is not computable in the general case.

Misleading Deep-Fake Detection with GAN Fingerprints

1 code implementation25 May 2022 Vera Wesselkamp, Konrad Rieck, Daniel Arp, Erwin Quiring

In particular, we show that an adversary can remove indicative artifacts, the GAN fingerprint, directly from the frequency spectrum of a generated image.

Machine Unlearning of Features and Labels

1 code implementation26 Aug 2021 Alexander Warnecke, Lukas Pirch, Christian Wressnegger, Konrad Rieck

In this paper, we propose the first method for unlearning features and labels.

Machine Unlearning

Dos and Don'ts of Machine Learning in Computer Security

no code implementations19 Oct 2020 Daniel Arp, Erwin Quiring, Feargus Pendlebury, Alexander Warnecke, Fabio Pierazzi, Christian Wressnegger, Lorenzo Cavallaro, Konrad Rieck

With the growing processing power of computing systems and the increasing availability of massive datasets, machine learning algorithms have led to major breakthroughs in many different areas.

BIG-bench Machine Learning Computer Security +1

Backdooring and Poisoning Neural Networks with Image-Scaling Attacks

no code implementations19 Mar 2020 Erwin Quiring, Konrad Rieck

By combining poisoning and image-scaling attacks, we can conceal the trigger of backdoors as well as hide the overlays of clean-label poisoning.

Misleading Authorship Attribution of Source Code using Adversarial Learning

1 code implementation29 May 2019 Erwin Quiring, Alwin Maier, Konrad Rieck

In this paper, we present a novel attack against authorship attribution of source code.

Authorship Attribution

Poisoning Behavioral Malware Clustering

no code implementations25 Nov 2018 Battista Biggio, Konrad Rieck, Davide Ariu, Christian Wressnegger, Igino Corona, Giorgio Giacinto, Fabio Roli

Clustering algorithms have become a popular tool in computer security to analyze the behavior of malware variants, identify novel malware families, and generate signatures for antivirus systems.

Clustering Computer Security +1

Static Exploration of Taint-Style Vulnerabilities Found by Fuzzing

no code implementations1 Jun 2017 Bhargava Shastry, Federico Maggi, Fabian Yamaguchi, Konrad Rieck, Jean-Pierre Seifert

In this paper, we use static template matching to find recurrences of fuzzer-discovered vulnerabilities.

Cryptography and Security Programming Languages Software Engineering

Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection

no code implementations28 Apr 2017 Ambra Demontis, Marco Melis, Battista Biggio, Davide Maiorca, Daniel Arp, Konrad Rieck, Igino Corona, Giorgio Giacinto, Fabio Roli

To cope with the increasing variability and sophistication of modern attacks, machine learning has been widely adopted as a statistically-sound tool for malware detection.

Cryptography and Security

Fraternal Twins: Unifying Attacks on Machine Learning and Digital Watermarking

no code implementations16 Mar 2017 Erwin Quiring, Daniel Arp, Konrad Rieck

This problem has motivated the research field of adversarial machine learning that is concerned with attacking and defending learning methods.

Autonomous Driving BIG-bench Machine Learning +3

From Malware Signatures to Anti-Virus Assisted Attacks

no code implementations19 Oct 2016 Christian Wressnegger, Kevin Freeman, Fabian Yamaguchi, Konrad Rieck

Although anti-virus software has significantly evolved over the last decade, classic signature matching based on byte patterns is still a prevalent concept for identifying security threats.

Cryptography and Security

When Coding Style Survives Compilation: De-anonymizing Programmers from Executable Binaries

3 code implementations28 Dec 2015 Aylin Caliskan, Fabian Yamaguchi, Edwin Dauber, Richard Harang, Konrad Rieck, Rachel Greenstadt, Arvind Narayanan

Many distinguishing features present in source code, e. g. variable names, are removed in the compilation process, and compiler optimization may alter the structure of a program, further obscuring features that are known to be useful in determining authorship.

Cryptography and Security

Toward Supervised Anomaly Detection

no code implementations23 Jan 2014 Nico Goernitz, Marius Micha Kloft, Konrad Rieck, Ulf Brefeld

Anomaly detection is being regarded as an unsupervised learning task as anomalies stem from adversarial or unlikely events with unknown distributions.

Active Learning Network Intrusion Detection +3

Cannot find the paper you are looking for? You can Submit a new open access paper.