Erasmus Addae

Bio: Erasmus Addae is an academic researcher from Austin Community College District. The author has contributed to research in topics: Cloud computing & Developing country. The author has an hindex of 2, co-authored 2 publications receiving 210 citations. Previous affiliations of Erasmus Addae include South Texas College.

Preliminary insight into cloud computing adoption in a developing country

Abstract: Purpose – The purpose of this paper is to investigate the determinants of cloud computing adoption (CCA) in a developing country context through the lens of the technology, organisation and environment (TOE) framework. Design/methodology/approach – The study was carried out using the quantitative research methodology based on a survey of 305 organisations from different industries in Ghana. Based on the TOE framework, a conceptual model consisting of ten hypotheses were proposed and tested through a confirmatory factor analysis and logistic regression analysis. Findings – The findings indicate that relative advantage, security concern, top management support, technology readiness, competitive pressure and trading partners’ pressure were the TOE factors found to be significant in CCA in a developing country context. Conversely, firm size, scope, compatibility and regulatory support were found to be insignificant. Originality/value – This study provides insights into CCA across different industries in a dev...

An Explainable Machine Learning Framework for Intrusion Detection Systems

Abstract: In recent years, machine learning-based intrusion detection systems (IDSs) have proven to be effective; especially, deep neural networks improve the detection rates of intrusion detection models. However, as models become more and more complex, people can hardly get the explanations behind their decisions. At the same time, most of the works about model interpretation focuses on other fields like computer vision, natural language processing, and biology. This leads to the fact that in practical use, cybersecurity experts can hardly optimize their decisions according to the judgments of the model. To solve these issues, a framework is proposed in this paper to give an explanation for IDSs. This framework uses SHapley Additive exPlanations (SHAP), and combines local and global explanations to improve the interpretation of IDSs. The local explanations give the reasons why the model makes certain decisions on the specific input. The global explanations give the important features extracted from IDSs, present the relationships between the feature values and different types of attacks. At the same time, the interpretations between two different classifiers, one-vs-all classifier and multiclass classifier, are compared. NSL-KDD dataset is used to test the feasibility of the framework. The framework proposed in this paper leads to improve the transparency of any IDS, and helps the cybersecurity staff have a better understanding of IDSs' judgments. Furthermore, the different interpretations between different kinds of classifiers can also help security experts better design the structures of the IDSs. More importantly, this work is unique in the intrusion detection field, presenting the first use of the SHAP method to give explanations for IDSs.