ML for Security Applications – KeyBridge
Our mission is increasing the security posture of both Oracle cloud customers and the Oracle's teams operating the cloud infrastructure.
Our research vision is of automated detection and mitigation of security events, allowing security experts to scale their efforts to large infrastructures such as the cloud. In the process, we working on data preparation and data exploration, statistical analysis and anomaly detection, alert generation and presentation of results.
Our analytics toolbelt includes techniques from data exploration, statistical analysis and deep learning. Working with application logs means that representation learning, machine learning for code and embedding techniques are fundamental research topics for us. So is explainable machine learning. For the scalable processing of the high volumes of logs we get, we are developing a machine learning pipeline framework that operates on top of various state-of-the-art libraries and follows a modular design.
Our team is a motley crew of researchers with diverse backgrounds in data analytics, machine learning, network and system design, and software development. We share a passion to develop reliable, innovative solutions to security problems with practical relevance. We place high value on a collaboration spirit, an inquisitive mindset and a drive to deliver high quality.
If you share our interests and values, we would be happy to welcome you in the family. Some ideas of directions in which an internship can go:
- Explorative: investigation of what machine learning techniques are applicable to a particular security problem.
- Research in ML: development of machine learning solutions for log encoding, source code generation, information retrieval, anomaly detection, building behavior profiles.
- Research in HCI: investigation of approaches towards data presentation for operational teams.
- Software development: development of scalable machine learning pipelines
Point of Contact
To apply, please send an email with the required information (see How to Apply above) to email@example.com.