Projects

The project will develop an intelligent monitoring system for OT networks, which are often classified as critical infrastructure. The system continuously checks equipment security, detects suspicious behavior, and alerts teams when intervention is needed, giving companies and authorities an early, clear overview of their security posture.

In this project, experts from the Alexandra Institute have joined forces with the Danish startup PrivacyMate to close the security gaps introduced by AI agents. The outcome will be an AI agent capable of handling data securely and accurately, as well as a tool that can be deployed internationally.

This project aims to develop an analysis tool that enables developers to detect and mitigate vulnerabilities in modern hybrid apps. The project will explore how static code analysis can be combined with dynamic analysis to detect sensitive data flows, for example from Java to JavaScript.

Satellites are increasingly vulnerable to cyberattacks, especially as they rely on interconnected ground systems and are often developed by smaller companies with limited cybersecurity resources. This project addresses this new reality, where satellites sit on the digital frontline, and sophisticated attacks must be quickly detected and mitigated.

In the quantum era, computers will solve mathematical problems far more efficiently, making post-quantum cryptography (PQC) essential. The challenge is to make PQC as practical and seamless as today’s systems. The project addresses that challenge by adapting threshold-based key management—where keys are shared across multiple parties—to remain secure in a quantum-resistant future.

Data normally flows safely through networks, but cyberattacks can subtly disrupt it—often unnoticed. Traditional NIDS based on synthetic data may miss such threats. This project combines machine learning with logic and differentiable logics trained on real data to create monitoring systems with digital intuition that detects even well-hidden attacks.

The project delivers a comprehensive framework that assesses risks and ensures traceability throughout the entire 3D printing process. It also incorporates encryption allowing partners to share designs without revealing intellectual property. The goal is to enable operators and suppliers to document high-quality production and detect deviations before they escalate into costly errors.