With the arrival of Ignacio Lacalle Úbeda at FOGUS for his secondment, it’s a great moment to spotlight the vital role of the Universitat Politècnica de València (UPV) in the AIAS project. Through its Distributed Real-Time Systems Lab (DRTSL), UPV brings deep technical expertise in cybersecurity, distributed AI, blockchain, and workload orchestration, making it a […]

Following our recent updates on Adversarial AI attack generation, the AIAS consortium is excited to continue informing the public about our work and unveil our cutting-edge research on Detection and Mitigation Techniques against adversarial AI threats. Current State-of-the-Art (SotA): AI-based methods have proven highly effective in detecting traditional cyberattacks, with techniques such as Support Vector

Following a well-deserved break for the Easter celebrations, the AIAS consortium is back to work, and we are excited to share an update on one of our core research areas: Adversarial AI Attack Generation. This research is crucial for developing innovative defense mechanisms to secure AI systems from emerging adversarial threats. Current State-of-the-Art (SotA): Adversarial

The AIAS project is driving forward its mission to enhance cybersecurity resilience by integrating Explainable AI (XAI) into its security platform and ensuring robust validation through real-world testing. These two vital research, technical, and innovation objectives form a critical part of our efforts to bridge the gap between AI-powered security solutions and human decision-making. XAI

The AIAS project is advancing its research and innovation through the development of two critical components: the Deception Layer and the AI-based Detection Module (AIDM). These modules play a pivotal role in strengthening the security posture of organizations against both traditional and adversarial AI cyber threats. Deception Layer – Predicting and Responding to Cyber Threats: