Enhancing Security in DevOps by Integrating Artificial Intelligence and Machine Learning

Authors

  • Penghao Liang Information Systems, Northeastern University, San Jose, CA, USA
  • Yichao Wu Computer Science, Northeastern University, San Jose, CA, USA
  • Zheng Xu Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, USA
  • Shilong Xiao Computer Science, Hebei Normal University, Shijiazhuang City, CN
  • Jiaqiang Yuan Information Studies, Trine University, Phoenix, AZ, USA

DOI:

https://doi.org/10.53469/jtpes.2024.04(02).05

Keywords:

Data security, DevOps, Machine Learning, Artificial Intelligence

Abstract

In modern software development and operations, DevOps (a combination of development and operations) has become a key methodology aimed at accelerating delivery, improving quality and enhancing security. Meanwhile, artificial intelligence (AI) and machine learning (ML) are also playing an increasingly important role in cybersecurity, helping to identify and respond to increasingly complex threats. In this article, we'll explore how AI and ML can be integrated into DevOps practices to ensure the security of software development and operations processes. We'll cover best practices, including how to use AI and ML for security-critical tasks such as threat detection, vulnerability management, and authentication. In addition, we will provide several case studies that show how these technologies have been successfully applied in real projects and how they have improved security, reduced risk and accelerated delivery. Finally, through this article, readers will learn how to fully leverage AI and ML in the DevOps process to improve software security, reduce potential risks, and provide more reliable solutions for modern software development and operations.

References

“Based on Intelligent Advertising Recommendation and Abnormal Advertising Monitoring System in the Field of Machine Learning”. International Journal of Computer Science and Information Technology, vol. 1, no. 1, Dec. 2023, pp. 17-23, https://doi.org/10.62051/ijcsit.v1n1.03.

Yu, Liqiang, et al. “Research on Machine Learning With Algorithms and Development”. Journal of Theory and Practice of Engineering Science, vol. 3, no. 12, Dec. 2023, pp. 7-14, doi:10.53469/jtpes.2023.03(12).02.

Liu, Bo, et al. "Integration and Performance Analysis of Artificial Intelligence and Computer Vision Based on Deep Learning Algorithms." arXiv preprint arXiv:2312.12872 (2023).

Yu, L., Liu, B., Lin, Q., Zhao, X., & Che, C. (2024). Semantic Similarity Matching for Patent Documents Using Ensemble BERT-related Model and Novel Text Processing Method. arXiv preprint arXiv:2401.06782.

Huang, J., Zhao, X., Che, C., Lin, Q., & Liu, B. (2024). Enhancing Essay Scoring with Adversarial Weights Perturbation and Metric-specific AttentionPooling. arXiv preprint arXiv:2401.05433.

Tianbo, Song, Hu Weijun, Cai Jiangfeng, Liu Weijia, Yuan Quan, and He Kun. "Bio-inspired Swarm Intelligence: a Flocking Project With Group Object Recognition." In 2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE), pp. 834-837. IEEE, 2023.DOI: 10.1109/mce.2022.3206678

Liu, B., Zhao, X., Hu, H., Lin, Q., & Huang, J. (2023). Detection of Esophageal Cancer Lesions Based on CBAM Faster R-CNN. Journal of Theory and Practice of Engineering Science, 3(12), 36–42. https://doi.org/10.53469/jtpes.2023.03(12).06

Liu, Bo, et al. "Integration and Performance Analysis of Artificial Intelligence and Computer Vision Based on Deep Learning Algorithms." arXiv preprint arXiv:2312.12872 (2023).

“Implementation of Computer Vision Technology Based on Artificial Intelligence for Medical Image Analysis”. International Journal of Computer Science and Information Technology, vol. 1, no. 1, Dec. 2023, pp. 69-76, https://doi.org/10.62051/ijcsit.v1n1.10.

K. Jin, Z. Z. Zhong and E. Y. Zhao, "Sustainable Digital Marketing Under Big Data: An AI Random Forest Model Approach," in IEEE Transactions on Engineering Management, vol. 71, pp. 3566-3579, 2024, doi: 10.1109/TEM.2023.3348991.

“Enhancing Computer Digital Signal Processing through the Utilization of RNN Sequence Algorithms”. International Journal of Computer Science and Information Technology, vol. 1, no. 1, Dec. 2023, pp. 60-68, https://doi.org/10.62051/ijcsit.v1n1.09.

Dong, Xinqi, et al. “The Prediction Trend of Enterprise Financial Risk Based on Machine Learning ARIMA Model”. Journal of Theory and Practice of Engineering Science, vol. 4, no. 01, Jan. 2024, pp. 65-71, doi:10.53469/jtpes.2024.04(01).09.

Tan, Kai, et al. “Integrating Advanced Computer Vision and AI Algorithms for Autonomous Driving Systems”. Journal of Theory and Practice of Engineering Science, vol. 4, no. 01, Jan. 2024, pp. 41-48, doi:10.53469/jtpes.2024.04(01).06.

“A Deep Learning-Based Algorithm for Crop Disease Identification Positioning Using Computer Vision”. International Journal of Computer Science and Information Technology, vol. 1, no. 1, Dec. 2023, pp. 85-92, https://doi.org/10.62051/ijcsit.v1n1.12.

Wang, Sihao, et al. “Diabetes Risk Analysis Based on Machine Learning LASSO Regression Model”. Journal of Theory and Practice of Engineering Science, vol. 4, no. 01, Jan. 2024, pp. 58-64, doi:10.53469/jtpes.2024.04(01).08.

Jin, Keyan. "Impacts of Word of Mouth (WOM) on E-Business Online Pricing." JGIM vol.31, no.3 2023: pp.1-17. http://doi.org/10.4018/JGIM.324813.

Wei, Kuo, et al. “Strategic Application of AI Intelligent Algorithm in Network Threat Detection and Defense”. Journal of Theory and Practice of Engineering Science, vol. 4, no. 01, Jan. 2024, pp. 49-57, doi:10.53469/jtpes.2024.04(01).07.

Zheng, Jiajian, et al. “The Credit Card Anti-Fraud Detection Model in the Context of Dynamic Integration Selection Algorithm”. Frontiers in Computing and Intelligent Systems, vol. 6, no. 3, Jan. 2024, pp. 119-22, https://doi.org/10.54097/a5jafgdv.

Qian, Jili, et al. “Analysis and Diagnosis of Hemolytic Specimens by AU5800 Biochemical Analyzer Combined with AI Technology”. Frontiers in Computing and Intelligent Systems, vol. 6, no. 3, Jan. 2024, pp. 100-3, https://doi.org/10.54097/qoseeQ5N.

Song, Tianbo, et al. “Development of Machine Learning and Artificial Intelligence in Toxic Pathology”. Frontiers in Computing and Intelligent Systems, vol. 6, no. 3, Jan. 2024, pp. 137-41, https://doi.org/10.54097/Be1ExjZa.

Du, Shuqian, et al. “Application of HPV-16 in Liquid-Based Thin Layer Cytology of Host Genetic Lesions Based on AI Diagnostic Technology Presentation of Liquid”. Journal of Theory and Practice of Engineering Science, vol. 3, no. 12, Dec. 2023, pp. 1-6, doi:10.53469/jtpes.2023.03(12).01.

Downloads

Published

2024-02-28

How to Cite

Liang, P., Wu, Y., Xu, Z., Xiao, S., & Yuan, J. (2024). Enhancing Security in DevOps by Integrating Artificial Intelligence and Machine Learning. Journal of Theory and Practice of Engineering Science, 4(02), 31–37. https://doi.org/10.53469/jtpes.2024.04(02).05

Issue

Vol. 4 No. 02 (2024): JTPES-2023-4-2

Section

Articles

License

Copyright (c) 2024 Penghao Liang, Yichao Wu, Zheng Xu, Shilong Xiao, Jiaqiang Yuan

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.