Identifying Vulnerabilities in Docker Image Code using ML Techniques

Abstract

A Docker container image can be defined as a lightweight, unattached, executable package of software that includes everything like code, runtime, system tools, system libraries and settings, needed to run an application, because of these features the container images are preferred over virtual machines. With this enormous usage, there is a lot of scope for the security issues arising in the container images. There are many open-source projects like Anchore, Clair that statically scan the container image’s docker file to find the vulnerabilities using databases like CVE, RedHat etc. Static analysis of container image main code is equally necessary to identify any vulnerabilities in the code and not only focus on the vulnerabilities based on OS level, as many malicious activities might take place if code is not scanned for any vulnerabilities. The main aim of the project is to create a static code analysing machine learning model to identify the vulnerable python libraries in container images.