| AUTOMATION OF ATTACK DETECTION |
RESEARCH CATEGORIES |
RESEARCH THEMES |
| IDENTIFICATION OF SOCIAL ENGINEERING ATTACKS |
T1.1: Extraction and structuring of semantics related to phishing techniques |
| T1.2: Characterisation of cyber scam techniques based on reinforcement learning and game theory |
| T1.3: Investigation of deep learning techniques for phishing detection |
| T1.4: Educational approaches to improve learning and prevent victimisation. |
| T1.5: Profiling cyber scams using biometric trait recognition (emotions) |
| FIGHT AGAINST FALSE INFORMATION |
T2.1: Collaborative expertise for the verification of information |
| T2.2: Assessing the trend of information on the Web |
| T2.3: Reducing the spread of false information in social networks |
| T2.4 : Detection of fake documents based on graph theory structures |
| CYBER RESILIENCE IoT ENVIRONMENTS |
T3.1: Detection of malicious applications in mobile OS |
| T3.2: Detect and fix software vulnerabilities |
| T3.3: Generation and structuring of knowledge base on attacks in IoT for their detection. |
| T3.4: Automation of nodes in decision making in IoT |
| T 3.5 : Security robustness based on blockchains |
