Preventing Cyber Attacks using Data Science and Machine Learning

As the Internet becomes more  prevalent in our daily lives through new devices and services such as IoT and Cloud, cyber attacks have become more sophisticated. Traditional approaches to cyber security such as perimeter protection and signature or rule based detection systems are no longer effective in detecting and preventing such attacks.  Using large amounts of data collected in the form of logs  and statistical modelling techniques such as Machine Learning, it is possible to develop highly accurate and robust threat detection systems. In this talk I will give an overview of how Data Science and Machine Learning are used to build cyber threat detection systems and present some examples from the Industry. 

About the speaker: Dr. Hari Koduvely is a senior data scientist with the CyberRes unit of Micro Focus in Ottawa, Canada. Prior to this he has worked in many companies such as Samsung R&D, Amazon and Infosys Labs. He has authored several publications and patents including the book, Learning Bayesian Methods using R. Hari has done his PhD in Tata Institute of Fundamental Research and postdoctoral research in Weizmann Institute Israel and Georgia Tech Atlanta before joining the Industry.