Search Results for author: Nils Ole Tippenhauer

Found 8 papers, 2 papers with code

Why Don't You Clean Your Glasses? Perception Attacks with Dynamic Optical Perturbations

no code implementations24 Jul 2023 Yi Han, Matthew Chan, Eric Wengrowski, Zhuohuan Li, Nils Ole Tippenhauer, Mani Srivastava, Saman Zonouz, Luis Garcia

We demonstrate that the dynamic nature of EvilEye enables attackers to adapt adversarial examples across a variety of objects with a significantly higher ASR compared to state-of-the-art physical world attack frameworks.

No Need to Know Physics: Resilience of Process-based Model-free Anomaly Detection for Industrial Control Systems

no code implementations7 Dec 2020 Alessandro Erba, Nils Ole Tippenhauer

Thus, the vulnerabilities we discovered in the anomaly detectors show that (despite an original good detection performance), those detectors are not able to reliably learn physical properties of the system.

Anomaly Detection

Assessing the Use of Insecure ICS Protocols via IXP Network Traffic Analysis

no code implementations2 Jul 2020 Giovanni Barbieri, Mauro Conti, Nils Ole Tippenhauer, Federico Turrin

Therefore, Shodan do not allow to understand the actual use of insecure industrial protocols on the Internet and the current security practices in ICS communications.

Cryptography and Security Networking and Internet Architecture

Constrained Concealment Attacks against Reconstruction-based Anomaly Detectors in Industrial Control Systems

1 code implementation17 Jul 2019 Alessandro Erba, Riccardo Taormina, Stefano Galelli, Marcello Pogliani, Michele Carminati, Stefano Zanero, Nils Ole Tippenhauer

In this work, we investigate different approaches to evade prior-work reconstruction-based anomaly detectors by manipulating sensor data so that the attack is concealed.

Anomaly Detection

HADES-IoT: A Practical Host-Based Anomaly Detection System for IoT Devices (Extended Version)

no code implementations3 May 2019 Dominik Breitenbacher, Ivan Homoliak, Yan Lin Aung, Nils Ole Tippenhauer, Yuval Elovici

The main advantage of HADES-IoT is its low performance overhead, which makes it suitable for the IoT domain, where state-of-the-art approaches cannot be applied due to their high-performance demands.

Cryptography and Security

Detection of Unauthorized IoT Devices Using Machine Learning Techniques

no code implementations14 Sep 2017 Yair Meidan, Michael Bohadana, Asaf Shabtai, Martin Ochoa, Nils Ole Tippenhauer, Juan Davis Guarnizo, Yuval Elovici

Based on the classification of 20 consecutive sessions and the use of majority rule, IoT device types that are not on the white list were correctly detected as unknown in 96% of test cases (on average), and white listed device types were correctly classified by their actual types in 99% of cases.

BIG-bench Machine Learning General Classification

Gamifying Education and Research on ICS Security: Design, Implementation and Results of S3

no code implementations10 Feb 2017 Daniele Antonioli, Hamid Reza Ghaeini, Sridhar Adepu, Martín Ochoa, Nils Ole Tippenhauer

In this work, we consider challenges relating to security for Industrial Control Systems (ICS) in the context of ICS security education and research targeted both to academia and industry.

Cryptography and Security

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