# AI-Driven Container Security Approaches for 5G and Beyond: A Survey

## TL;DR

This survey reviews machine learning-based container security methods in cloud and 5G environments, highlighting recent research efforts to enhance security through AI-driven intrusion detection, malware detection, and placement strategies.

## Contribution

It provides a comprehensive overview of machine learning approaches in container security, emphasizing recent advancements and categorizing existing research.

## Key findings

- Machine learning enhances detection of novel threats in container security.
- Security strategies are divided into rule-based and AI-driven methods.
- Recent research focuses on intrusion detection, malware detection, and container placement.

## Abstract

The rising use of microservices based software deployment on the cloud leverages containerized software extensively. The security of applications running inside containers as well as the container environment itself are critical infrastructure in the cloud setting and 5G. To address the security concerns, research efforts have been focused on container security with subfields such as intrusion detection, malware detection and container placement strategies. These security efforts are roughly divided into two categories: rule based approaches and machine learning that can respond to novel threats. In this study, we have surveyed the container security literature focusing on approaches that leverage machine learning to address security challenges.