Hodaya works in Accenture Lab as an applied researcher. She holds am MSc degree in Information Systems Engineering from Ben-Gurion University. Her research areas focuses on Machine-learning (ML), Knowledge graphs, Natural langue processing (NLP) and cyber-security.
Talks and Events
Using a Hybrid AI Approach to Automatically Extract Recommendations from Public Knowledge Repositories
The emerging landscape of deep learning and knowledge graph technologies provides vast opportunities to use public repositories as a source of knowledge for recommendations. Often, the knowledge is represented as a knowledge graph, and a recommendation regarding an entity instance is extracted by querying it. For example, a knowledge graph representation can be used for describing cybersecurity domain entities, such as adversarial techniques and their countermeasures. We can use this graph to recommend a specific countermeasure given a system-specific detected technique. This approach raises few challenges. First, how to correlate between the instance entity and the suitable object in the knowledge graph? Second, how to extract the recommendations from the graph? We developed a hybrid AI approach that addressed these challenges by utilizing deep learning-based language models and graph traversal algorithms. In this session we will demonstrate how we automatically categorized vulnerability descriptions discovered in specific systems according to their adversarial techniques and recommended relevant countermeasures.
Track: Deep Learning for and with Knowledge Graphs
- Graph Data Science
- Deep Learning for and with Knowledge Graphs
- Graph Machine Learning