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:

As the AIAS project progresses, we are excited to share some of our core research, technical, and innovation objectives that will drive our efforts toward securing AI systems and enhancing organizational resilience against adversarial threats. Key Components of the AIAS Platform: Core Activities: Through these objectives, AIAS aims to push the boundaries of cybersecurity innovation