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The article first gives some descriptions of IDS for vehicles that can be found in several related surveys. After this, the article has an adequate introduction to the subject, and the authors make it clear what they expect from this study. The section on background knowledge describes in detail how IDS schemes are trained, validated, and tested using datasets. The article offers sufficient information about networks in vehicles that are vulnerable to a variety of cyberattacks. Figure 6 helps the readers to understand the high-level ideas of different deep-learning network architectures, such as RNN, CNN, GAN, and DNN.
This is survey research. In this article, the authors use a systematic literature review process (planning, conducting, and reporting) to collect more detailed articles on the deep learning techniques designed by the IDS. The five RQ (Research Questions) defined in the planning stage help to organize the overall article structure more straightforwardly. The use of snowballing process offers a thorough examination of related research works targeting on intrusion detection, anomaly detection, and in-vehicle network. Also, it's great that this article provides a comprehensive collection of deep learning-based IDS schemes published between 2016 and 2022.
As a result of this work, one major novelty is that the authors present a fine-grained taxonomy based on the neural network architectures in order to classify the state-of-the-art DL-IDS schemes on their capabilities. The article compares and critically examines the investigated solutions by examining their methods, datasets, and evaluation metrics. At the end of the article, the authors identify possible future research directions for improving DL-based IDS performance. The conclusion part is appropriately stated and connects to the original question investigated.
The revised version addressed all my questions/concerns in the first round of review.
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The authors have addressed all my concerns.
The reviewers have made some recommendations on issues to be addressed for the revised manuscript.
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The article first gives some descriptions of IDS for vehicles that can be found in several related surveys. After this, the article has an adequate introduction to the subject, and the authors make it clear what they expect from this study. The section on background knowledge describes in detail how IDS schemes are trained, validated, and tested using datasets. The article offers sufficient information about networks in vehicles that are vulnerable to a variety of cyberattacks. Figure 6 helps the readers to understand the high-level ideas of different deep-learning network architectures, such as RNN, CNN, GAN, and DNN.
This is survey research. In this article, the authors use a systematic literature review process (planning, conducting, and reporting) to collect more detailed articles on the deep learning techniques designed by the IDS. The five RQ (Research Questions) defined in the planning stage help to organize the overall article structure more straightforwardly. The use of snowballing process offers a thorough examination of related research works targeting on intrusion detection, anomaly detection, and in-vehicle network. Also, it's great that this article provides a comprehensive collection of deep learning-based IDS schemes published between 2016 and 2022.
As a result of this work, one major novelty is that the authors present a fine-grained taxonomy based on the neural network architectures in order to classify the state-of-the-art DL-IDS schemes on their capabilities. The article compares and critically examines the investigated solutions by examining their methods, datasets, and evaluation metrics. At the end of the article, the authors identify possible future research directions for improving DL-based IDS performance. The conclusion part is appropriately stated and connects to the original question investigated.
Please list more existing methods based on traditional machine learning that cannot handle the security risks very well.
Please add more references to some statements made in the article, such as the significant advantages of IVN IDSs, and the capability of deep learning networks in identifying sophisticated attacks and zero-day attacks.
Please explain more about why the IDS schemes are limited to detecting packet-level anomalies.
Have you deeply investigated the usage of a combination of more than one DL network? The article mentioned that the combination of CNNs and RNNs is used most in hybrid-based IDS schemes, are there any other combinations used in any specific schemes?
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This paper gives a survey on deep learning-based in-vehicle network intrusion detection methods. It covers many recent approaches and performs some comparison. The paper was well-written. Some problems need to be solved to improve the quality of the paper.
(1) It is suggested to cover more literature published in 2023, so as to increase the impact of the paper.
(2) The paper can show some comparison experimental results on public benchmark datasets and conduct analysis.
(3) The size of different datasets can be shown.
(4) The future directions can be organized in a more structural manner to highlight different topics.
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