All reviews of published articles are made public. This includes manuscript files, peer review comments, author rebuttals and revised materials. Note: This was optional for articles submitted before 13 February 2023.
Peer reviewers are encouraged (but not required) to provide their names to the authors when submitting their peer review. If they agree to provide their name, then their personal profile page will reflect a public acknowledgment that they performed a review (even if the article is rejected). If the article is accepted, then reviewers who provided their name will be associated with the article itself.
The reviewers have no further comments, and they both recommend that the manuscript be accepted as is.
[# PeerJ Staff Note - this decision was reviewed and approved by Sedat Akleylek, a 'PeerJ Computer Science' Section Editor covering this Section #]
The paper can be accepted
The paper can be accepted
The paper can be accepted
The paper can be accepted
no comment
no comment
no comment
no comment
Two reviewers have commented on this submission. They seem to agree that this manuscript may be publishable after a major revision. The first reviewer raises a number of important points regarding the novelty of this work, and in particular asks a comparison of the performance of the new method with the state of the art for the problem.
Manuscript ID Submission ID 102709v1
This paper is related to reviewing the manuscript titled " Intelligent algorithmic framework for detection and mitigation of BeiDou spoofing attacks in VANETs"
This research examines the critical issue of spoofing in the BeiDou Navigation Satellite System (BDS) within Vehicular Ad-hoc Networks (VANETs), offering advanced strategies for detection, tolerance, and management to enhance vehicular communication security. With the growing reliance on BDS for accurate vehicle positioning, spoofing presents significant risks to vehicular safety and traffic management. The authors use a hybrid machine learning approach, integrating Autoencoders and Long Short-Term Memory (LSTM) networks, along with the advanced cryptographic method ‘Attribute-Based Encryption (ABE)’, to create a robust anti-spoofing framework.
Firstly, Although the proposed study is successful in terms of organization, presentation, content and results, major revision given in the following items need to be performed.
1) Provide the major numerical findings and conclusions of the study in the summary section.
2) The mathematical model of proposed LSTM Model must be validated. Why is the recommended model a hybrid model? How is it strikingly different from others?
3) Standard LSTM model equations are given in Equations 10-13. The innovation and contribution of the proposed deep network-based model must be given by the proposed model.
4) The proposed method lacks any basis regarding VANET energy consumption.
5) Increase the resolution of figures.
6) Several operations were carried out on the VANET network with the method of attack detection and mitigation. However, it seems that performance analyzes that are widely used in attack detection and prevention studies, such as error checking, collision rate, efficiency, and data loss rate, are not given in the experimental part.
7) In addition, the proposed model should be compared with new methods.
As above
My decision is major revision. I do not see any harm in publishing the manuscript once the above revisions are made.
1.The abstract and conclusion of this article need to be rewritten because the author did not clearly state what solutions or methods the article proposed, or what problems it solved.
2. This article should conduct a complexity analysis and discussion on the proposed algorithm.
3. This article lacks a theoretical feasibility analysis of the proposed solution.
The paper should provide a more detailed analysis of the reasons behind the experimental results.
no comment
1.The abstract and conclusion of this article need to be rewritten because the author did not clearly state what solutions or methods the article proposed, or what problems it solved.
2. This article should conduct a complexity analysis and discussion on the proposed algorithm.
3. This article lacks a theoretical feasibility analysis of the proposed solution.
All text and materials provided via this peer-review history page are made available under a Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.