Enhancing Industrial Cyber Security, Focusing on Formulating a Practical Strategy for Making Predictions through Machine Learning Tools in Cloud Computing Environment
Round 1
Reviewer 1 Report
This article researched to improve the potential of techniques and algorithms Machine learning (ML) (Support Vector Machine, XGBoost, and Artificial Neural Networks) in enhancing cloud computing security in the industry. Focused on identifying gaps in utilizing ML techniques in cloud cyber security. Moreover, this study aims at developing a practical strategy for predicting the employment of machine learning in an Industrial Cloud environment regarding trust and privacy issues. The study created a new dataset and used a feature selection approach (Random Forest Feature importance) to choose the most important features. The 11 most suitable features were used in the experiments.
The choice of the proposed methods is justified. This approach is relevant. The research is of practical importance.
The conclusions correspond to the presented evidence and arguments, and they correspond to the basic question. The references appropriately. The tables meet the requirements. The pictures and formulas need to be improved.
1. "Figure 2. Flowsheet of Applied Method "- does not comply with the ISO standard. Maybe the UML diagrams are more visual to describe how the algorithm works.
Author Response
Responses to the Review Comments
Journal: electronics (ISSN 2079-9292)
Manuscript ID: electronics-2435757
Title of Paper: Enhancing Industrial Cyber Security, Focusing on Formulating a Practical Strategy for Making Predictions through Machine Learning Tools in Cloud Computing Environment
Date: 01 June 2023
We want to express our sincere appreciation to the reviewers for their valuable feedback on our manuscript. Their thoughtful comments and constructive suggestions have significantly improved our work's quality.
We have carefully considered all the feedback from four reviewers and made substantial revisions to the original manuscript. We have addressed all concerns raised in the review report and have made the necessary improvements to refine the manuscript further. Additionally, we have ensured that this revised version of the manuscript meets the journal's publication requirements.
Below, we provide our detailed responses to the reviewers' comments. We are confident that our revisions will address all the concerns raised, and we hope that the revised manuscript will be satisfactory for publication.
Responses to Reviewer 1
This article researched to improve the potential of techniques and algorithms in Machine learning (ML) (Support Vector Machine, XGBoost, and Artificial Neural Networks) in enhancing cloud computing security in the industry. They were focused on identifying gaps in utilizing ML techniques in cloud cyber security. Moreover, this study aims at developing a practical strategy for predicting the employment of machine learning in an Industrial Cloud environment regarding trust and privacy issues. The study created a new dataset and used a feature selection approach (Random Forest Feature importance) to choose the most important features. The 11 most convenient features were used in the experiments. The choice of the proposed methods is justified. This approach is relevant. The research is of practical importance. The conclusions correspond to the presented evidence and arguments, and they correspond to the basic question. The references are appropriate. The tables meet the requirements. The pictures and formulas need to be improved.
Q1. "Figure 2. Flow sheet of Applied Method "- does not comply with the ISO standard. The UML diagrams may be more visual to describe how the algorithm works.
>> Our response: The authors appreciate the reviewer's remarks, and Author revised the flow sheet diagram and complies with ISO standards.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Dear authors,
I have been invited to review your paper. Attached you will find several comments and questions that I hope will contribute to enhance the clarity and quality of your paper.
Thank you and best regards,
Anonymous reviewer
Comments for author File: Comments.pdf
Author Response
Responses to the Review Comments
Journal: electronics (ISSN 2079-9292)
Manuscript ID: electronics-2435757
Title of Paper: Enhancing Industrial Cyber Security, Focusing on Formulating a Practical Strategy for Making Predictions through Machine Learning Tools in Cloud Computing Environment
Date: 01 June 2023
We want to express our sincere appreciation to the reviewers for their valuable feedback on our manuscript. Their thoughtful comments and constructive suggestions have significantly improved our work's quality.
We have carefully considered all the feedback from four reviewers and made substantial revisions to the original manuscript. We have addressed all concerns raised in the review report and have made the necessary improvements to refine the manuscript further. Additionally, we have ensured that this revised version of the manuscript meets the journal's publication requirements.
Below, we provide our detailed responses to the reviewers' comments. We are confident that our revisions will address all the concerns raised, and we hope that the revised manuscript will be satisfactory for publication.
Responses to Reviewer 2
- Does the introduction provide sufficient background and include all relevant references?
>> Our response: The Author appreciates the reviewer's comment, and by considering the comment, the introduction is further reviewed and improved in terms of sufficient background and relevant references. All the less relevant references have been removed.
- Are all the cited references relevant to the research?
>> Our response: All the cited references are reviewed again thoroughly, and the citation is improved by incorporating more relevant references and eliminating irrelevant ones. By appreciating and considering the reviewer's comment, now the manuscript contains all the most relevant cited references from the up-to-date approaches.
- Is the research design appropriate?
>> Our response: The reviewer has demanded the improvement of the research design. By appreciating and considering the comment, the design is improved by further elaborating the method in text and the flow sheet diagram.
- Are the methods adequately described?
>> Our response: The methodology is further improved by adding more details regarding the used experimental setup for more clarification of the research design and the results. The information regarding the platform for the research design is updated along with the model's information and feature selection. By appreciating and considering the reviewer's comment, the methods are reviewed and described more accurately for complete clarification.
- Are the results presented?
>> Our response: The result and discussion portion are thoroughly reviewed and improved by adding more information regarding the feature selection and model's performances. Now, the results are presented so that the desired information and the research objectives are completely comprehensible.
- Do the results support the conclusions?
>> Our response: The Author appreciates the comment, and the conclusions are reviewed and further described to indicate a clear picture of the results as demanded by the reviewer. The performance of the machine learning models in indicating the industrial cloud cyber security concerns, issues, and gaps is accurately concluded from the results, which fulfills the objectives of this study.
Author Response File: Author Response.pdf
Round 2
Reviewer 2 Report
Dear authors,
Thank you for the revised version of your paper.
Best regards,
Anonymous reviewer
I would recommend to have the text formally edited.
