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

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

PI: Helen Gu; Researchers: Olufogorehan Tunde-Onadele (Fogo)

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.

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 continued to refine our approaches for aggregated learning and application classification. We saw improved detection results while training using larger number of containers. We further refined our application classification algorithm using random forest learning methods and can achieve over 96% accuracy now. We also completed attack type classification implementation and conducted extensive experiments.

COMMUNITY ENGAGEMENTS

None.

EDUCATIONAL ADVANCES:

One PhD students Fogo (Olufogorehan Tunde-Onadele) is currently supported by the grant.