Security of Foundation Models for Code

We explore the vulnerability of FMs to poisoning and evasion attacks, and the possible defenses at the various stages of the model lifecycle: pre-training, fine-tuning and deployment.

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Combining Security Protection Mechanisms for Machine Learning Models

We investigate how to maintain the effectiveness of different protection mechanisms when they are applied to the same ML model.

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Large Language Models For Cyber Threat Detection

We leverage LLMs to generate and prioritise security alerts from network and host logs.

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Advanced Persistent Threat Dectection

We identify APTs early in their lifetime cycle, combining DNS, HTTP, and TCP/IP level features. We also plan to incorporate host features, and leverage GNNs to detect APTs in Critical Information Infrastructure.

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Concept drift in Deep Learning-based security applications

We extend the life of DL-based NIDS by enhancing them with concept drift detection and adaptation.

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Improving Intrusion Dataset Quality with Causal Methods

We automate experiment design to capture better network intrusion datasets, leveraging causal analysis.

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Reinforcement Learning for Web Security

We use reinforcement learning to automate the fuzzing of web applications and browsers, to indentify a range of vulnerabilities (XSS, SQLi, buffer overflow, etc).

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Software Vulnerability Detection using Deep Learning

We use GRUs, GNNs and Transformers to identify source code vulnerabilities in C/C++ and PHP.

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