In a world in which organizations are continually trying to deliver their software as quickly as possible. Keeping security strong throughout the development process is becoming a pressing challenge. Usually, security is put in at the end of the development process, typically resulting in high and extensive cost in order to rectify security loopholes in the software. DevSecOps changes that by weaving security into every level of the DevOps workflows and developing closer collaborations between the developers, operations, and security teams. This translates to software that is designed and developed security-first. This paper is a systematic literature review to examine the integration of security in the DevOps procedures, the demands for successful DevSecOps implementations, and the hurdles that institutions face when implementing it. Automated security testing, infrastructure as code, threat modeling and container security are used in DevSecOps practices to ensure the security of every level of the devops operations, as evidenced by an in-depth analysis of 22 scientific peer-reviewed articles published between 2020 and 2025. However, the review also uncovered some persistent challenges so as some of the solutions to these problems. Lack of existing literature is a gap with calls to develop more standardized frameworks and tools that can be used in the DevSecOps implementations to make them more readily adopted by organizations. Through investigating this area of growing interest in DevOps, it becomes possible to contribute to the knowledge of incorporating security more proactively into DevOps channels with the aim of creating more secure software systems.
Oluwatobi Kuye, Ridwan Kolapo, T. Atoyebi et al.· FUDMA Journal of Sciences· 0 citations
-Large firms utilize Security Information and Event Management (SIEM) systems that allow them to gather, normalize, correlate, and analyze security events that, in turn, come from endpoints, networks, identity platforms, cloud services, databases, and applications. Even if traditional rule-based SIEM is indeed useful for regulatory compliance and has an understanding of the attack patterns, it has its limitations due to the existence of high event volume, heterogeneous logs, false positive, alert fatigue, and multi-stage attacks. That's why this article is such a great read! They have done a deep analysis of the various machine learning-based enterprise SIEM systems available. PRISMA 2020 guided the reporting of study selection, while an adapted Waterfall process organized requirements definition, protocol design, search, screening, quality appraisal, extraction, synthesis, and reporting. A careful analysis of thirty studies was conducted with the help of both descriptive and thematic synthesis. The notable findings in the study indicate that the area of research is mostly concerned with log anomaly detection and threat detection. The best techniques for dealing with data that has correct labels & decisions by an analyst are supervised and ensemble methods; on the other hand, the semi-supervised, self-supervised, deep, and transformer-based methods are appropriate for the applications of the large unlabelled log streams; graph-based methods remain as the best option for the events that happen together; and the explainable artificial intelligence is the one that enables trust in analysts. The attention to incidents, system response and defense mechanisms as well as privacy, model drift, adversarial robustness, and the actual security operations center are less supported by involved statistics. It is stated in the article that no one specific machine learning technique is appropriate for each SIEM task. The success of an enterprise deployment is based on implementing the appropriate techniques depending on the security function, data quality, label availability, explanation requirements, and analyst workflow.
Joshua Ahuose Omoighe, T. Atoyebi, Ridwan Kolapo et al.· Iconic research and engineer...· 0 citations
Financial transaction fraud is a significant problem in the fields of digital banking, credit card transactions, online payment, and mobile financial services. This study systematically reviewed the existing literature on supervised machine learning techniques used for financial fraud detection. The review included peer-reviewed papers published between 2020 and 2026 and followed the PRISMA framework, with Parsifal used to assist the search management, screening, eligibility assessment, and data extraction. A total of 503 records were obtained from IEEE Xplore, Scopus, Web of Science, ScienceDirect, and ACM Digital Library. After removing 129 duplicates, screening 374 records, and assessing 107 full-text articles, a total of 55 studies were accepted in the final review. The findings indicated that financial fraud detection relied more on supervised learning which was the method of choice, especially in cases where labelled data of fraudulent and legitimate transactions were known. Credit/debit card fraud was the most common form of fraud, contributing to 40 studies (72.7%) of the reviewed literature. Methodologically, ensemble, boosting, and hybrid classifiers were the most common methods, occurring in 17 studies (30.9%), followed by deep/hybrid representation learning in 12 studies (21.8%) and classical supervised machine learning comparisons in 10 studies (18.2%). The review further indicated that the model performance not only depends on the algorithm choice but also on preprocessing, feature selection, class imbalance handling, and evaluation metrics. The most commonly reported metric was accuracy, which occurred in 52 studies (94.5%), whereas recall/sensitivity was present in 50 studies (90.9%), precision was in 48 studies (87.3%), F1-score was in 47 studies (85.5%), ROC-AUC/AUC was in 42 studies (76.4%), and confusion matrix was in 38 studies (69.1%). The study ends by recommending that effective supervised fraud detection is a complete methodological pipeline and not just isolated algorithm comparison.
Blessing Bologi, Ridwan Kolapo, Temitope Olufunmi Atoyebi· International journal of res...· 0 citations
This study focuses on leveraging Random Forest model for PCOS prediction using only clinical and biochemical data, enhanced with Shapley Additive Explanations (SHAP) for model interpretability.
C. Nweke, Prema A. Kirubakaran, Ridwan Kolapo· International Journal of Inn...· 0 citations
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