Cristina Oliveira

MagIC (Management Information Research Centre) at NOVA University Lisbon is at the forefront of interdisciplinary research, where advanced data science techniques intersect with critical real-world challenges. One of our key contributions is in the field of cybersecurity, where we leverage cutting-edge machine learning (ML) and artificial intelligence (AI) to enhance security measures, detect threats, and build resilient digital infrastructures.

Key Contributions of MagIC to Cybersecurity

1. Anomaly Detection and Threat Intelligence
   Our researchers develop ML-based anomaly detection models capable of identifying unusual behavior in network traffic, detecting zero-day attacks, and preventing data breaches. By analyzing large-scale datasets, our systems can differentiate between normal and malicious activity with high precision.

2. AI-driven Fraud Detection
   MagIC integrates deep learning models to combat financial fraud, identity theft, and cyber-enabled crimes. Our AI-driven fraud detection techniques improve risk assessment and predictive modeling, aiding financial institutions and regulatory bodies in real-time fraud prevention.

3. Privacy-Preserving AI for Secure Data Sharing
   We advance the use of federated learning and synthetic data generation to enhance data security while maintaining privacy. These approaches enable organizations to collaborate on cybersecurity models without exposing sensitive information, ensuring compliance with GDPR and other regulations.

4. Adversarial Machine Learning and AI Robustness
   MagIC researchers investigate adversarial ML techniques to protect AI systems from manipulation and bias. By designing robust AI architectures, we enhance the security of critical applications in finance, healthcare, and smart cities.

5. Blockchain and AI for Secure Transactions
   We explore the convergence of blockchain technology and AI to improve cybersecurity in decentralized systems. Our work in smart contract security, cryptographic protocols, and distributed ledger analysis helps mitigate risks associated with blockchain vulnerabilities.