Daily Papers

Security is becoming a pivotal point in cloud platforms. Several divisions, such as business organisations, health care, government, etc., have experienced cyber-attacks on their infrastructures. This research focuses on security issues within Continuous Integration and Deployment (CI/CD) pipelines in a cloud platform as a reaction to recent cyber breaches. This research proposes a blockchain-based solution to enhance CI/CD pipeline security. This research aims to develop a framework that leverages blockchain's distributed ledger technology and tamper-resistant features to improve CI/CD pipeline security. The goal is to emphasise secure software deployment by integrating threat modelling frameworks and adherence to coding standards. It also aims to employ tools to automate security testing to detect publicly disclosed vulnerabilities and flaws, such as an outdated version of Java Spring Framework, a JavaScript library from an unverified source, or a database library that allows SQL injection attacks in the deployed software through the framework.

Deep learning solutions for vulnerability detection proposed in academic research are not always accessible to developers, and their applicability in industrial settings is rarely addressed. Transferring such technologies from academia to industry presents challenges related to trustworthiness, legacy systems, limited digital literacy, and the gap between academic and industrial expertise. For deep learning in particular, performance and integration into existing workflows are additional concerns. In this work, we first evaluate the performance of CodeBERT for detecting vulnerable functions in industrial and open-source software. We analyse its cross-domain generalisation when fine-tuned on open-source data and tested on industrial data, and vice versa, also exploring strategies for handling class imbalance. Based on these results, we develop AI-DO(Automating vulnerability detection Integration for Developers' Operations), a Continuous Integration-Continuous Deployment (CI/CD)-integrated recommender system that uses fine-tuned CodeBERT to detect and localise vulnerabilities during code review without disrupting workflows. Finally, we assess the tool's perceived usefulness through a survey with the company's IT professionals. Our results show that models trained on industrial data detect vulnerabilities accurately within the same domain but lose performance on open-source code, while a deep learner fine-tuned on open data, with appropriate undersampling techniques, improves the detection of vulnerabilities.