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

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 continue our research on real time container vulnerability discovery. In order to understand the tradeoffs between different anomaly detection techniques and their effectiveness on detecting real world container vulnerabilities, we implemented and adapted a set of commonly used unsupervised machine learning techniques and compare their anomaly detection accuracies. We wrote a paper on the comparison results and submitted it to a security conference.

COMMUNITY ENGAGEMENTS

None.

EDUCATIONAL ADVANCES:

One PhD student Olufogorehan Tunde-Onadele is currently supported by the grant.