Diploma in Anomaly and Attack Detection in Graphs

The Diploma in Anomaly and Attack Detection in Graphs focuses on the analysis of complex data structured in graphs, applying machine learning and data mining techniques to identify anomalous patterns and detect cyberattacks. It explores the use of advanced algorithms for fraud detection, social media security, and cybersecurity in critical infrastructure. The program includes the analysis of social networks, transportation networks, and financial networks, with a focus on graph visualization and results interpretation. The diploma provides practical skills in using tools and frameworks such as Neo4j, NetworkX, and Python libraries for graph analysis, including graph machine learning and threat modeling. Students will acquire the ability to develop mitigation and response strategies for security incidents, preparing them for roles such as graph security analysts, graph data scientists, and cybersecurity consultants, strengthening the protection of critical data and systems.

Target keywords (natural in the text): graphs, anomaly detection, attacks, machine learning, cybersecurity, social media security, fraud detection, graph analysis.