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After careful consideration and peer review, I am pleased to inform you that we are willing to consider your manuscript for publication. We greatly appreciate the effort you put into your work, and the constructive feedback provided by the reviewers has helped to improve the quality and clarity of your submission.
[# PeerJ Staff Note - this decision was reviewed and approved by Vicente Alarcon-Aquino, a PeerJ Section Editor covering this Section #]
Thank you for submitting your manuscript to our journal. Following the review process, Reviewer 1 has provided valuable feedback that highlights some areas where improvements are needed. Below are the remaining issues within the manuscript that need to be addressed:
Basic Reporting:
The typo "BukaGinin" should be corrected. Line 109 reads: "Moreover, the mathematical framework of the bukaGinin is composed of a Dataset D consisting..."
In Figure 3, please ensure that the legend is placed above the graphs and consider increasing its size for better readability.
Formatting:
Formulas that currently take up an entire line would be more readable if they were centered. Please revise the formatting accordingly.
Addressing these issues will enhance the clarity and presentation of your manuscript. Once the revisions are complete, please resubmit the revised manuscript along with a point-by-point response to the reviewers' comments.
There is a "BukaGinin" typos. Line 109: "Moreover, the mathematical framework of the bukaGinin is composed of a Dataset D consisting"
Figure 3: legend above the graphs and a little larger
Formulas that take up an entire line would be more readable if they were centered
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Grammar and Readability:
The paper requires a revision of the English, and some parts are unclear.
Specific Comments: My specific comments concerning this manuscript are:
- The abstract does not highlight the specifics of the research or findings but contains too much background information. Some details of the research would be nice for example numbers addressing the sample, data, percentage improvement, etc.. Remove some of the background material and add some details of the research. Moreover, it is good to provide some specifics (e.g., sample size, dataset size, numbers from results, etc.).
- Although a novel combination might be allowed, it is necessary to highlight the contribution of such a combination from both methodological and empirical perspectives. Also, it is required to provide technical details of the proposed methods as much as possible and in-depth explanations of method selections.
- The innovation of the paper seems limited. The proposed method is a straightforward combination of existing techniques, which makes it less innovative. Also, more details about the proposed method should be provided.
- I suggest adding a clear research objective or research questions in the introduction section and specifying what the main research problem or hypothesis is addressed
- Improve the explanation about how the comment and mention relations are extracted from the dataset, and what criteria are used to filter out irrelevant or noisy data
- I recommend proofreading to make reading smoother.
- There are several papers that have addressed similar problems, but it is necessary to further highlight the novelty between the proposed study and the related literature.
- Starting from the previous works, I suggest introducing a table to summarize the most recent works and to highlight the novelty of the proposed work.
- Please add more recent references. Certainly, there has been more recent (within the last two years) research on this topic published in information science and/or computer science outlets. An academic search on the topic (using keywords from the manuscript’s title) shows that there is recent work in this area. Therefore, authors must update their literature review.
- There needs to be an explicit research objective(s) and/or research question(s) stated, preferably as a separate section. This helps readers find out what the research is trying to address.
- The reference list needs tidying up, as there are references missing items or formatting issues. Please be consistent with the formatting and use some standard formatting style.
- The evaluation is weak. Please consider using a more convincing way to evaluate the proposed method.
- The discussion of the results does not highlight the strengths and weaknesses of the proposed approach.
**PeerJ Staff Note:** Please ensure that all review, editorial, and staff comments are addressed in a response letter and that any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate.
**Language Note:** The Academic Editor has identified that the English language must be improved. PeerJ can provide language editing services - please contact us at copyediting@peerj.com for pricing (be sure to provide your manuscript number and title). Alternatively, you should make your own arrangements to improve the language quality and provide details in your response letter. – PeerJ Staff
There are two "BukaGinin" typos.
Figure 3: I suggest removing the legend for each chart and leaving a general one out of the graphs. Furthermore, also, I would use 4 decimal places for the values on the dots, trying not to let them overlap with the lines.
Figure 2: it does not allow us to understand the order of the processes
Table 4: split the table into multiple tables. One table where you give an id (or algorithm name) to each work (id, work), and a table for each dataset where you can compare BukaGini work with others, on the same dataset.
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This work seems doesn't add novelty to your previous work on the definition of BukaGini. It appears that these are just tests on datasets of a particular area of interest (IDS).
For the data imputation process, have you explored the possibility of using specialized algorithms?
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This study proposes the BukaGini algorithm to overcome the feature interaction issue in Intrusion Detection Systems for enhanced performance. The proposal was extensively evaluated and several metrics were presented. However, I have the following concerns:
1. Introduction Section:
(a) I suggest to improve the problem statement you are addressing since several gap researches are mentioned.
(b) Authors must clarify the novelty of this work based on the BukaGini algorithm within ML compared to those works using this same approach.
(c) Relevant and current works of Machine Learning based Intrusion Detection systems are not considering in this section. How is this study different from studies using complex DL/ML models for improved feature analysis in IDSs?
2. The BuckaGini Algorithm Section: As it is described in the Introduction Section, it seems a practice of a well known algorithm namely BuckaGini within ML algorithms. Authors must include and support this section with references.
3. Materials & Methods Section:
(a) For a better understanding of the feature analysis using BuckaGini algorithm, it is suggested that the authors include a description of input data, as well as the description of the features extracted or the most common features used in this work.
(b) For purposes of reproducibility and future comparison with the state of the art, it is suggested that the authors add more details of the feature interaction step.
4. Dataset Section: Authors must include references of the datasets.
5. Performance Metrics Section: Equations 1-5 must be supported by some References. Moreover, Stability score is missing.
6. Results and Discussion Section:
(a) It is suggested that authors extend the discussion of results. Consider that datasets used comprise different types of features and number of instances.
(b) UNSWNB15 dataset also includes network flow and biometric features?
(c) There is an overfitting or an underfitting problem in the work proposed by Ismail et al. 2022?
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