Coordinated Machine Learning-Based Vulnerability & Security Patching for Resilient Virtual Computing Infrastructure (Template)

PI(s), Co-PI(s), Researchers:

PI: Helen Gu

HARD PROBLEM(S) ADDRESSED
This refers to Hard Problems, released November 2012.

Resilient Architectures

Our research aims at aiding administrators of virtualized computing infrastructures in making services more resilient to security attacks through applying machine learning to reduce both security and functionality risks in software patching by continually monitoring patched and unpatched software to discover vulnerabilities and triggering proper security updates.

PUBLICATIONS
Papers written as a result of your research from the current quarter only.

None.

KEY HIGHLIGHTS
Each effort should submit one or two specific highlights. Each item should include a paragraph or two along with a citation if available. Write as if for the general reader of IEEE S&P.
The purpose of the highlights is to give our immediate sponsors a body of evidence that the funding they are providing (in the framework of the SoS lablet model) is delivering results that "more than justify" the investment they are making.

Containers have become increasingly popular for deploying applications in cloud computing infrastructures. However, our previous study has shown that containers are prone to various security attacks.

In this quarter, we conducted an empirical study on the effectiveness of various container vulnerability detection schemes to understand the challenges in real world container vulnerability discovery.

COMMUNITY ENGAGEMENTS

The PI attended and made a presentation about our project at the Science of Security Lablet Kickoff and Quarterly Meeting at College Park.

EDUCATIONAL ADVANCES:

Two PhD students have been supported by the grant. The PI added a Science of Security related module in the CSC 724 class she regularly teaches.