AI-Driven Continuous Integration and Continuous Deployment in Software Engineering

Mohammed, Abdul Sajid; Saddi, Venkata Ramana; Gopal, Santhosh Kumar; Dhanasekaran, S.; Naruka, Mahaveer Singh

doi:10.1109/icdt61202.2024.10489475

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2024

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Cited by 2 publications

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Prediction of electricity consumption using an innovative deep energy predictor model for enhanced accuracy and efficiency

Ragupathi,

Dhanasekaran,

Vijayalakshmi

et al. 2024

Energy Reports

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Prediction of electricity consumption using an innovative deep energy predictor model for enhanced accuracy and efficiency

Ragupathi,

Dhanasekaran,

Vijayalakshmi

et al. 2024

Energy Reports

View full text Add to dashboard Cite

Advancing Software Security and Reliability in Cloud Platforms through AI-based Anomaly Detection

Saleh,

Sayem,

Madhavji

et al. 2024

Proceedings of the 2024 on Cloud Computing Security Workshop

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Continuous Integration/Continuous Deployment (CI/CD) is fundamental for advanced software development, supporting faster and more efficient delivery of code changes into cloud environments. However, security issues in the CI/CD pipeline remain challenging, and incidents (e.g., DDoS, Bot, Log4j, etc.) are happening over the cloud environments. While plenty of literature discusses static security testing and CI/CD practices, only a few deal with network traffic pattern analysis to detect different cyberattacks. This research aims to enhance CI/CD pipeline security by implementing anomaly detection through AI (Artificial Intelligence) support. The goal is to identify unusual behaviour or variations from network traffic patterns in pipeline and cloud platforms. The system shall integrate into the workflow to continuously monitor pipeline activities and cloud infrastructure. Additionally, it aims to explore adaptive response mechanisms to mitigate the detected anomalies or security threats. This research employed two popular network traffic datasets, CSE-CIC-IDS2018 and CSE-CIC-IDS2017. We implemented a combination of Convolution Neural Network (CNN) and Long Short-Term Memory (LSTM) to detect unusual traffic patterns. We achieved an accuracy of 98.69% and 98.30% and generated log files in different CI/CD pipeline stages that resemble the network anomalies affected to address security challenges in modern DevOps practices, contributing to advancing software security and reliability.

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AI-Driven Continuous Integration and Continuous Deployment in Software Engineering

Cited by 2 publications

References 15 publications

Prediction of electricity consumption using an innovative deep energy predictor model for enhanced accuracy and efficiency

Prediction of electricity consumption using an innovative deep energy predictor model for enhanced accuracy and efficiency

Advancing Software Security and Reliability in Cloud Platforms through AI-based Anomaly Detection

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