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.

Yuhang Lin, Olufogorehan Tunde-Onadele and Xiaohui Gu, "CDL: Classified Distributed Learning for Detecting Security Attacks in Containerized Applications", Proc. of Annual Computer Security Applications Conference (ACSAC), Austin, TX, December, 2020.

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.

In this quarter, we presented Self-Path, a self-triggering patching framework for containerized applications at ACSOS 2020. We completed CDL, a classified distributed learning framework for security attack detection in container-based systems. CDL has been accepted for presentation at ACM ACSAC 2020, a security conference. We further started to investigate automatic security patch generation techniques and enhanced security attack detection solutions using reinforcement learning.

COMMUNITY ENGAGEMENTS

None.

EDUCATIONAL ADVANCES:

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