Deep Learning, Computer Networks, and Cybersecurity
Reichman University
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Deep Learning, Computer Networks, and Cybersecurity
Reichman University
Tal Shapira, Ph.D., conducting research in the fields of deep learning, computer networks, and cybersecurity. Currently a Post-Doc at the School of Computer Science, Reichman University. Graduated magna cum laude with a P.hD. and an M.Sc. in Electrical Engineering from the School of Electrical Engineering, Tel-Aviv University, and received the B.Sc. degree in physics and minor in mathematics from the Hebrew University of Jerusalem.
Co-Founder & Chief Scientist at Reco. Graduate of the Talpiot Excellence Program, with in-depth knowledge of data science, deep learning, big data, and cybersecurity R&D, with a demonstrated history of working in the military industry – head of a cybersecurity group within the Prime Minister’s Office (Israel Defense Award), and as the Head of Algorithms at Guardian Optical Technologies (acquired by Gentex).
With the continuous increase in reported Common Vulnerabilities and Exposures (CVEs), security teams are overwhelmed by vast amounts of data, which are often analyzed manually, leading to a slow and inefficient process. To address cybersecurity threats effectively, it is essential to establish connections across multiple security entity databases, including CVEs, Common Weakness Enumeration (CWEs), and Common Attack Pattern Enumeration and Classification (CAPECs). In this study, we introduce a new approach that leverages the RotatE [4] knowledge graph embedding model, initialized with embeddings from Ada language model developed by OpenAI [3]. Additionally, we extend this approach by initializing the embeddings for the relations.
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