arXiv subjects

Sabbir M. Saleh

Publications and source records attributed to Sabbir M. Saleh.

3 recordsLinked to original sources

arXiv2025

Enhancing Cloud Security through Topic Modelling

Protecting cloud applications is critical in an era where security threats are increasingly sophisticated and persistent. Continuous Integration and Continuous Deployment (CI/CD) pipelines are particularly vulnerable, making innovative security approaches essential. This research explores the application of Natural Language Processing (NLP) techniques, specifically Topic Modelling, to analyse security-related text data and anticipate potential threats. We focus on Latent Dirichlet Allocation (LDA) and Probabilistic Latent Semantic Analysis (PLSA) to extract meaningful patterns from data sources, including logs, reports, and deployment traces. Using the Gensim framework in Python, these methods categorise log entries into security-relevant topics (e.g., phishing, encryption failures). The identified topics are leveraged to highlight patterns indicative of security issues across CI/CD's continuous stages (build, test, deploy). This approach introduces a semantic layer that supports early vulnerability recognition and contextual understanding of runtime behaviours.

cs.CR↗

arXiv2025

A Systematic Literature Review on Continuous Integration and Deployment (CI/CD) for Secure Cloud Computing

As cloud environments become widespread, cybersecurity has emerged as a top priority across areas such as networks, communication, data privacy, response times, and availability. Various sectors, including industries, healthcare, and government, have recently faced cyberattacks targeting their computing systems. Ensuring secure app deployment in cloud environments requires substantial effort. With the growing interest in cloud security, conducting a systematic literature review (SLR) is critical to identifying research gaps. Continuous Software Engineering, which includes continuous integration (CI), delivery (CDE), and deployment (CD), is essential for software development and deployment. In our SLR, we reviewed 66 papers, summarising tools, approaches, and challenges related to the security of CI/CD in the cloud. We addressed key aspects of cloud security and CI/CD and reported on tools such as Harbor, SonarQube, and GitHub Actions. Challenges such as image manipulation, unauthorised access, and weak authentication were highlighted. The review also uncovered research gaps in how tools and practices address these security issues in CI/CD pipelines, revealing a need for further study to improve cloud-based security solutions.

cs.SE↗

arXiv2024

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

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.

cs.SE↗