The Advances in Graph Neural Networks Meet Cybersecurity

19th November 2024, Tuesday (11:00-12:40), Room 1

Session Chair : Sarra Cherbal, Setif 1 University, Algeria

ABSTRACT

Graph Neural Networks are a powerful and versatile class of Deep learning models that have revolutionized the way we approach problems involving relational data. They are highly influential models that have shown great success in a variety of domains. From airline flights to recommender systems to disaster management to neuronal interactions in the brain to cybersecurity, graphs are ubiquitous in the real world. Graph Neural Networks (GNNs) have emerged as a powerful tool in cybersecurity, particularly for tasks like vulnerability identification and intrusion detection. By leveraging graph structures, GNNs can capture the complex relationships within data, making them effective in learning representations from graph-structured data for cyber defense. GNN models address the limitations of machine learning and deep learning approaches by providing a more robust and abstract view of systems, essential for understanding structural patterns of attacks and vulnerabilities. In this tutorial, researchers and students will explore and master various graph embedding methods through hands-on exercises. They will gain insights into the strategies for mastering how to implement GNNs, achieving high accuracy in representing graph data, and swiftly deploying effective solutions. By completing this tutorial, the attendees will gain a solid understanding of the subject, both theoretically and practically.

BIOGRAPHY

Dr. Ahlem Drif is an Associate Professor/Researcher in the Computer Sciences Department of Ferhat Abbas University of Sétif 1 (UFAS), Algeria. She has engineering degree in Advanced Information Systems (2002) and Magister degree in Computer Science (2006) from University of Sétif. She hold a Ph.D from UFAS, under the supervision of Ex-Professor Abdellah Boukerram and Professor Silvia. Giordano, Department of Innovative Technologies - SUPSI - Switzerland. She received the university accreditation (HDR) in 2021. With over 14 years of research experience, Dr. Ahlem Drif's primary area of expertise lies in Data Sciences; encompassing Intelligent Trajectory applications, Deep learning, Recommender Systems, AI in Medicine, Explainable XAI for Healthcare, and Social Computing based LLM. She published more than 40 peer-reviewed research articles in international journals and conferences. She serves as a reviewer for many international journals and conferences.