A Deep Dive into Container Security Challenges, Strategies, and Solutions
- DOI
- 10.2991/978-94-6463-787-8_38How to use a DOI?
- Keywords
- Container Security; Cloud Scheduling; Kubernetes; Orchestration; Vulnerabilities
- Abstract
Container technology has transformed cloud computing by allowing lightweight, scalable, and portable applications. Yet, with the rise of containers in multitenant cloud settings, security problems in container scheduling have emerged as a major worry. The emergence of container orchestration platforms like Kubernetes, Docker Swarm, and Apache Mesos has brought about fresh challenges in securely managing containerized workloads. This article examines the security issues in container scheduling, focusing on resource isolation, vulnerabilities in orchestration tools, risks in inter-container communication, and data security. Additionally, it assesses approaches to reducing risks, like improved resource segregation, safe orchestration methods, and encrypted data management. The research also points out new developments in the industry, such as AI-powered anomaly detection, zero-trust setups, and policy-based scheduling, providing insight on improving security in containerized cloud settings. This research aims to help improve container scheduling frameworks by tackling these challenges.
- Copyright
- © 2025 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Kanika Sharma AU - Parul Khurana PY - 2025 DA - 2025/07/17 TI - A Deep Dive into Container Security Challenges, Strategies, and Solutions BT - Proceedings of the Recent Advances in Artificial Intelligence for Sustainable Development (RAISD 2025) PB - Atlantis Press SP - 484 EP - 495 SN - 1951-6851 UR - https://doi.org/10.2991/978-94-6463-787-8_38 DO - 10.2991/978-94-6463-787-8_38 ID - Sharma2025 ER -