Sep. 04, 2019


Speaker: Aviad Cohen, Ben-Gurion University of the Negev

Title:  A Triple-Layered Machine Learning-Based Methodology for Enhancing the Security of Email Eco-system

In this research, we introduce a triple-layered machine learning based methodology for enhancing the security of email ecosystem. All three layers employ machine learning methods; each layer addresses the security of different level in the email eco-system. The first layer detects malicious non-executable files attached to emails. The second layer detects malicious emails by analyzing the entire email structure quickly and independent of any external resource. The third layer detects, in a trusted manner, whether the email server has been compromised by a malware. For each layer, we present a published journal paper, which propose a novel approach, and prove the feasibility and applicability of each layer of the methodology.

About the speaker:

Aviad Cohen is a senior security researcher at the Malware-Lab, Cyber Security Research Center (CSRC) at Ben-Gurion University of the Negev. Aviad pursues his Ph.D. studies between 2015 and 2019 in BGU's Department of Software and Information Systems Engineering. His research is aimed at the development of a triple-layered machine learning-based methodology for enhancing the security of E-mail ecosystem. He is a co-author of several papers dealing with the analysis and detection of malicious non-executable files, malicious emails and compromised virtual machine. His main areas of interest are cyber security, machine learning and data science.