Kilian Tscharke – Cybersecurity-Blog

Quantum and Classical AI Security: How to Build Robust Models Against Adversarial Attacks

The rise of quantum machine learning (QML) brings exciting advancements such as higher levels of efficiency or the potential to solve problems intractable for classical computers. Yet how secure are quantum-based AI systems against adversarial attacks compared to classical AI? A study conducted by Fraunhofer AISEC explores this question by analyzing and comparing the robustness of quantum and classical machine learning models under attack. Our findings about adversarial vulnerabilities and robustness in machine learning models form the basis for practical methods to defend against these attacks, which are introduced in this article.

Anomaly Detection with Quantum Machine Learning – Identifying Cybersecurity Issues in Datasets

Since the release of ChatGPT, the popularity of Machine Learning (ML) has grown immensely. Besides Natural Language Processing (NLP) anomaly detection is an important branch of data analysis whose goal is to identify observations or events that deviate from the rest of the data. At Fraunhofer AISEC, cybersecurity experts explore Quantum Machine Learning methods for anomaly detection. One approach is based on the classification of quantum matter while a second method uses a type of Quantum Support Vector Machine with a kernel that is calculated on a quantum computer. This blog post explains the fundamentals of anomaly detection and shows the two approaches being pursued by the Quantum Security Technologies group at Fraunhofer AISEC.