Cyber Security Seminar on Machine Learning in Threat Detection
Machine Learning in Threat Detection: Challenges and Possible Solutions
Machine learning (ML) has been applied in many technology areas, e.g., image recognition, recommender systems, and many more. Its application in cybersecurity, especially in threat detection, has gained more traction lately, with many security vendors introducing it into their products to win more customers. However, the use of ML in threat detection comes with challenges that need to be addressed before the industry can effectively reap its benefits in this area. This talk will highlight the challenges of applying ML to threat detection and suggest ways to solve these highlighted issues.
(Meeting ID: 921 4750 2861, Passcode: 126910)
A cybersecurity researcher/engineer, Obinna Igbe, currently works in the tech sector and has worked for multiple research institutions and fortune 50 tech companies. He holds an MS and a PhD in cybersecurity from the City University of New York (CUNY) and has published multiple articles and conference papers in the security domain. His research interests span across the security of critical infrastructures like smart grids and the design and implementation of systems to combat cyber threats targeting organizations (with a considerable focus on insider threats).