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Article
Peer-Review Record

# Intelligent Network Intrusion Prevention Feature Collection and Classification Algorithms

Algorithms 2021, 14(8), 224; https://doi.org/10.3390/a14080224
by
Reviewer 1: Anonymous
Reviewer 2: Juvenal Rodríguez-Reséndiz
Algorithms 2021, 14(8), 224; https://doi.org/10.3390/a14080224
Received: 29 June 2021 / Revised: 21 July 2021 / Accepted: 24 July 2021 / Published: 26 July 2021

Round 1

Reviewer 1 Report

The authors used KDD data set.  Its very old data set.  There are so many new data sets are available.  They can use it in future.

Author Response

Point 1: I found that sections 2 & 3 should be re‐organized and be shortened. It may be easier for the readers if the authors define properly the mixture of regression model and the class‐ membership equation first before moving to the computation of the GINI and of the Polarization of subgroups. Sections 2.1 and 2.2 are too long and can be significantly reduced. In section 2.1 the authors assume the condition uk > uj, but this does not appear anywhere else in the calculation of the mixture of regression model. After equation (10) all the other equations are not numbered.

Response 1: Lengthy sections noticed as given above are re-organized and shortened as per your suggestion. All equation variable definition and derivations are validated

Point 2: The probability for a given country to be in a class should be the proportion of observations (households) in country that belong to the income class k. On page 9, the first equation (it would be easier for the reader if the equation is numbered) is not exactly the proportion of people because the authors take the sum of the probability. The interpretation of the equation in not obvious. Normally, after estimating a mixture of regression model we have for each observation its estimated probabilities to be classified into the different classes identified. What is often done is to classify a given observation into the class where its estimated probability is higher. In many software this is also the method used that gives us the proportion of people in each of the classes. The authors should explain the equation on page 9 and how to interpret it. Alternatively, they may use the proportion approach which will make the interpretation easier.

Response 2: Updated as per the recommendation. In page 9, the significance of equation is included. (Since the updates from the preceding queries alter the page number, required updates are happened in page 10)

Reviewer 2 Report

The authors present the article entitled “Intelligent Network Intrusion Prevention Feature Collection and Classification Algorithms.” The article is hard to read and needs a major revision according to the following concerns:

Grammar and spelling in the manuscript present some errors. Please check.

Please check the author's guidelines. The names must be Firstname Lastname format.

Corresponsal author is not mentioned or identified.

The title is not attractive for the readers and is not concise. I suggest changing the title by including the ISTID method in it.

The abstract presents a lack of information about the method proposed, and results are missing. Would you mind checking the author guidelines?

The novelty and the objective of the paper are not clear.

The manuscript does not present a methodology section.

Section 2 does not present a review of related works.

Section 3 needs a hard revision. It is hard to follow the proposed method by the following concerns:

-ISTID architecture is mentioned in section 3 but is not mentioned before. It is expected to describe this method before in a general form to introduce it.

- The methodology to implement the algorithm is not clear and confusing. It could help to use a flowchart to describe the algorithm.

-ISTID is first mentioned in subsection 3.1 and is defined again in subsection 3.2.

-Pseudocode is out of format; please check author guidelines.

Please remove the background color of the plots.

I suggest adding a Discussion section to compare the proposed method with the related works.

Highlight the novelty in the abstract.

Conclusion section: Please restructure the conclusion according to the objective of the paper and future works.

Please add more references to support the work.

My primary concern is that it can not be seen the novelty in work. Which is the scientific novelty? Also, in the bibliography section, related works will be compared with the proposal. Herein, avoid the conference citation since they are not well peer-reviewed. Also, add Algorithms Journal references since there are lots of works in this journal.

Author Response

Point 1: I found that sections 2 & 3 should be re‐organized and be shortened. It may be easier for the readers if the authors define properly the mixture of regression model and the class‐ membership equation first before moving to the computation of the GINI and of the Polarization of subgroups. Sections 2.1 and 2.2 are too long and can be significantly reduced. In section 2.1 the authors assume the condition uk > uj, but this does not appear anywhere else in the calculation of the mixture of regression model. After equation (10) all the other equations are not numbered.

Response 1: Lengthy sections noticed as given above are re-organized and shortened as per your suggestion. All equation variable definition and derivations are validated

Point 2: The probability for a given country to be in a class should be the proportion of observations (households) in country that belong to the income class k. On page 9, the first equation (it would be easier for the reader if the equation is numbered) is not exactly the proportion of people because the authors take the sum of the probability. The interpretation of the equation in not obvious. Normally, after estimating a mixture of regression model we have for each observation its estimated probabilities to be classified into the different classes identified. What is often done is to classify a given observation into the class where its estimated probability is higher. In many software this is also the method used that gives us the proportion of people in each of the classes. The authors should explain the equation on page 9 and how to interpret it. Alternatively, they may use the proportion approach which will make the interpretation easier.

Response 2: Updated as per the recommendation. In page 9, the significance of equation is included. (Since the updates from the preceding queries alter the page number, required updates are happened in page 10)

Round 2

Reviewer 2 Report