A Meta-Model to Predict and Detect Malicious Activities in 6G-Structured Wireless Communication Networks

Round 1

Reviewer 1 Report

A unique meta-machine learning model for anomaly detection networks was developed in this paper. The five main stages of the proposed meta-model are as follows: the accumulated datasets (NSL KDD, UNSW NB15, CIC IDS17, and SCE CIC IDS18) comprise the initial stage. It is a well-structured paper with interesting results. 

(1) The conclusion and motivation of the work should be added in a clearer way.

(2) How is the complexity of the proposed method? Please describe in detail.

(3) Correct typological mistakes and mathematical errors

(4) In order to further highlight the introduction, some advised references should be added to the paper for improving the review part and the connection with the literature. For example, https://doi.org/10.1088/1361-6501/ac9a61

https://doi.org/10.1016/j.ymssp.2022.109422

https://doi.org/10.3389/fendo.2022.1057089

(5) Figure 1. is not very clear, please revise it to be clear.

(6) In Figure 3. The Meta-model structure, there used six models, how to divid these data?  Which data is sent to these different models？

(7) In Section 4(Discussion and Limitations), provide more discussion.

Author Response

Thank you for the valuable comments, please find attached the authors' response report.

Author Response File: Author Response.docx

Reviewer 2 Report

Please see the attached file.

Comments for author File: Comments.pdf

Author Response

Thank you for the valuable comments, please find attached the authors' response report.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

This paper can be accepted now.

Reviewer 2 Report

The authors have basically addressed my concerns, no further comments.