Container Security Using Algorithmic Approach

Abstract

Container technology is one of the fastest growing technologies. However, when it comes to the security of containers, their vulnerability has increased in line with its popularity. Because when users use containers, they may unknowingly perform actions that can make their Docker environment insecure. So, we need to verify security of entire container environment. In this paper, we describe a mechanism that can validate security level of container environment by using algorithmic approach.

Country : Sri Lanka

1 Ramanayaka R.P.N.M2 Abeywickrama J.A.S.T

  1. Faculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  2. Faculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka

IRJIET, Volume 7, Issue 11, November 2023 pp. 503-510

doi.org/10.47001/IRJIET/2023.711066

References

  1. Sharma, P., Kumar, M., & Sharma, H. K. (2022, October 1). Comprehensive analyses of image forgery detection methods from traditional to deep learning approaches: an evaluation. Multimedia Tools and Applications; Springer Science+Business Media. https://doi.org/10.1007/s11042-022-13808-w.
  2. CTF series: Binary exploitation¶ (no date) CTF Series : Binary Exploitation - tech.bitvijays.com. Available at: https://bitvijays.github.io/LFC-BinaryExploitation.html (Accessed: 03 November 2023).
  3. Yamashita, R., Nishio, M., Gian, R. K., & Togashi, K. (2018, June 22). Convolutional neural networks: an overview and application in radiology. Insights Into Imaging; Springer Nature. https://doi.org/10.1007/s13244-018-0639-9.
  4. Bourouis, S., Alroobaea, R., Alharbi, A., Andejany, M., & Rubaiee, S. (2020, November 1). Recent Advances in Digital Multimedia Tampering Detection for Forensics Analysis. Symmetry; Multidisciplinary Digital Publishing Institute. https://doi.org/10.3390/sym12111811.
  5. Yamashita, R., Nishio, M., Gian, R. K., & Togashi, K. (2018, June 22). Convolutional neural networks: an overview and application in radiology. Insights Into Imaging; Springer Nature. https://doi.org/10.1007/s13244-018-0639-9.
  6. Zeng, P., Tong, L., Liang, Y., Zhou, N., & Wu, J. (2022, October 17). Multitask Image Splicing Tampering Detection Based on Attention Mechanism. Mathematics; Multidisciplinary Digital Publishing Institute. https://doi.org/10.3390/math10203852.
  7. Dong, Y., Wu, P., Wang, S., & Liu, Y. (2023, February 1). ShipGAN: Generative Adversarial Network based simulation-to-real image translation for ships. Applied Ocean Research; Elsevier BV. https://doi.org/10.1016/j.apor.2022.103456.
  8. Noreen, I., Muneer, M. S., & Gillani, S. (2022, October 20). Deep fake attack prevention using steganography GANs. PeerJ; PeerJ, Inc. https://doi.org/10.7717/peerj-cs.1125.
  9. Chatterjee, A. K., & Ahmed, B. S. (2022, August 1). IoT anomaly detection methods and applications: A survey. Internet of Things; Elsevier BV.https://doi.org/10.1016/j.iot.2022.100568.
  10. J. Jang-Jaccard and S. Nepal, “A survey of emerging threats in cybersecurity,” J. Comput. Syst. Sci., vol. 80, no. 5, pp. 973–993, Aug. 2014, doi: 10.1016/J.JCSS.2014.02.005.
  11. N. S. Publication, “Nist special publication 400-95,” Nist Spec. Publ.
  12. M. Di Giuseppe, J. C. Perry, T. A. Prout, and C. Conversano, “Editorial: Recent Empirical Research and Methodologies in Defense Mechanisms: Defenses as Fundamental Contributors to Adaptation,” Front. Psychol., vol. 12, p. 5405, Dec. 2021, doi: 10.3389/FPSYG.2021.802602/BIBTEX.
  13. “Docker security.” https://docs.docker.com/engine/security/ (accessed Apr. 07, 2023).
  14. “Container Escape: All You Need is Cap (Capabilities).” https://www.cybereason.com/blog/container-escape-all-you-need-is-cap-capabilities (accessed Apr. 07, 2023).
  15. Simonyan and Zisserman,. (n.d.). ResearchGate. https://www.researchgate.net/figure/An-example-of-CNN-architecture-VGG-Simonyan-and-Zisserman-2014-Colour-online_fig1_325974939.
  16. N. R. C. (US) W. B. Committee, J. N. Pato, and L. I. Millett, “Cultural, Social, and Legal Considerations,” 2010, Accessed: Apr. 07, 2023. [Online]. Available: https://www.ncbi.nlm.nih.gov/books/NBK21989.
  17. NSA and CISA, “Kubernetes Hardening Guide,” Nsa/Cisa, no. March, 2022, [Online]. Available: https://media.defense.gov/2021/Aug/03/2002820425/-1/-1/0/CTR_Kubernetes_Hardening_Guidance_1.1_20220315.PDF.
  18. N. Odell and C. A. Shue, “Developing Single Use Server Containers A Major Qualifying Project,” 2020.
  19. “What is Role-Based Access Control | RBAC vs ACL & ABAC | Imperva.” https://www.imperva.com/learn/data-security/role-based-access-control-rbac/ (accessed Apr. 07, 2023).
  20. “What is RBAC? | Definition from TechTarget.” https://www.techtarget.com/searchsecurity/definition/role-based-access-control-RBAC (accessed Apr. 07, 2023).
  21. Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A. Q., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021, March 31). Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data; Springer Science+Business Media. https://doi.org/10.1186/s40537-021-00444-8.
  22. Czakon, J. (2023, September 5). F1 Score vs ROC AUC vs Accuracy vs PR AUC: Which Evaluation Metric Should You Choose? neptune.ai. https://neptune.ai/blog/f1-score-accuracy-roc-auc-pr-auc.
  23. Sharma, P., Kumar, M., & Sharma, H. K. (2022, October 1). Comprehensive analyses of image forgery detection methods from traditional to deep learning approaches: an evaluation. Multimedia Tools and Applications; Springer Science+Business Media. https://doi.org/10.1007/s11042-022-13808-w.
  24. Yamashita, R., Nishio, M., Gian, R. K., & Togashi, K. (2018, June 22). Convolutional neural networks: an overview and application in radiology. Insights Into Imaging; Springer Nature. https://doi.org/10.1007/s13244-018-0639-9.
  25. “Twistlock Container Security | Prisma Cloud Review.” https://www.esecurityplanet.com/products/twistlock/ (accessed Apr. 07, 2023).