User abnormal behavior recommendation via multilayer network

Chengyun Song; Weiyi Liu; Zhining Liu; Xiaoyang Liu

doi:10.1371/journal.pone.0224684

Peer Review History

Original SubmissionJuly 1, 2019
4 Sep 2019 Decision Letter - He Debiao, Editor PONE-D-19-18509 Privacy-Preserving Recommendation of User Abnormal Behavior Under Multilayer Network PLOS ONE Dear Dr. song, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. We would appreciate receiving your revised manuscript by Oct 19 2019 11:59PM. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. 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We will change the online submission form on your behalf. Please know it is PLOS ONE policy for corresponding authors to declare, on behalf of all authors, all potential competing interests for the purposes of transparency. PLOS defines a competing interest as anything that interferes with, or could reasonably be perceived as interfering with, the full and objective presentation, peer review, editorial decision-making, or publication of research or non-research articles submitted to one of the journals. Competing interests can be financial or non-financial, professional, or personal. Competing interests can arise in relationship to an organization or another person. Please follow this link to our website for more details on competing interests: http://journals.plos.org/plosone/s/competing-interests Additional Editor Comments (if provided): [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: Yes ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes ****** 3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #2: Yes ****** 4. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes ****** 5. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: I am particularly satisfied with content and structure of this article. It suggest that the author has taken enough time to prepare the manuscript. The few observations from me is highlighted in the article. Reviewer #2: The manuscript presents important information and deserves to be published in Plos One. Before publication, I suggest the following modifications: Page 1, line 6-7 Change “machine learning algorithms” for “machine learning (ML) algorithms” Page 1, line 7 Change “SVM” for “Support Vector Machine (SVM)” Page 1, line 9 Change “machine learning algorithms” for “ML algorithms” Page 1, line 13 Change “IP adresses” for “Internet Protocol (IP) addresses” Page 1, line 15 Change “machine learning models” for “ML models” Page 2, line 18 Change “machine learning methods” for “ML methods” Page 2, line 27 Change “machine learning algorithms” for “ML algorithms” Page 2, line 30 “…over time1.” Confirm in the Plos One standards if footnotes can be used. Page 2, line 39 Change “machine learning” for “ML” Page 2, line 55 “The system is available at https://github.com/StoneSongLucky/Private Preserving Outlier Behavior Detection” The link is unavailable. Page 2, line 56-62 Delete: “The remainder of the paper is organized as follows. In Section 2, we discuss recent work on user abnormal behavior recommendation. Section 3 describes the whole proposed model, and describes network construction (Section 3.3), network analysis (Section 3.4), outlier recommendation (Section 3.5) and a visualization practical system (Section 3.6). In Section 4, we present a thorough evaluation of the proposed privacy-preserving recommendation system. Finally, we conclude and describe future work in Section 5.” Page 3, line 65 Change “machine learning” for “ML” Page 3, line 71 Change “machine learning (ML) models” for “ML models” - The abbreviation should appear the first time the word is cited in the manuscript (as per the previous changes I am suggesting). Page 10, line 262 Change “machine learning-based algorithms” for “ML-based algorithms” Page 10, line 266 Change “machine learning-based algorithms” for “ML-based algorithms” Page 11, line 283 Change “machine learning-based algorithms” for “ML-based algorithms” ****** 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review?** For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: Yes: MKA Abdulrahman Reviewer #2: No [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Please note that Supporting Information files do not need this step. Attachments Attachment Submitted filename: PONE-D-19-18509_reviewer.pdf-reveiwed.pdf https://doi.org/10.1371/journal.pone.0224684.r001
Revision 1
8 Oct 2019 Author Response Response to Reviewers and Editors' Comments & List of Changes in the Revised Paper Title: Privacy-Preserving Recommendation of User Abnormal Behavior Under Multilayer Network Authors: Chengyun Song, Weiyi Liu, Zhining Liu, Xiaoyang Liu ------------------------------------------------------------------------------------------------------- The authors thank the reviewers and editors for their comments and suggestions. We have revised the paper following the comments and suggestions. Following is our response and also the list of changes that we have made in the revised version. The changes, including newly added parts, are in red font in the revised manuscript. Review Comments to Author: Reviewer 1: 1. Reconcile the 15% and 85% satisfaction mentioned in the conclusion Response: Sorry to bring some mistakes and ambiguous here. We meant to say that according to our proposed practical system is highly friendly to uses, as we use “85% satisfaction” to point out there are a large amount of users that do accept our proposed system, and only “15%” of users which are NOT satisfied. To eliminate the ambiguous, we use “up to 85% satisfaction” in the abstract and conclusion 2. Citation missing Response: In the paper, we define a simple and way (the percentages of abnormal nodes) to evaluate the security scores in multilayer network. So, there is no citation here. 3. From the decision box " If True". The flow chart did not say anything about 'if false". Marking the suspicious node is not enough but creating a library of suspicious nodes to be verify for subsequent logins Response: For “If false”, the program will ignore the current user. We have updated this figure in the revised paper. And if the system has found a “suspicious” node, it will raise an alert to alarm the user, as the final decision should be made by user himself. 4. Give the scenario of medium level noise in this method Response: Here we use “medium” as the opposite of “High”. In fact, due to the fact that our proposed method (privacy-preserving) and the ML-based algorithms needs all the information to learn the user behavior, these methods have possibilities to absorb noises information as well. Hence, we have replaced term “medium” as “Not Effected”. 5. Cite standard that support this assertion Response: We have added the cite paper to support the assertion in the revised version. 6. Reconcile with abstract Response: Sorry to bring ambiguous here. We have used “up to 85% satisfaction” in the abstract which is same to the conclusion. Reviewer 2: 1. Page 1, line 6-7 Change “machine learning algorithms” for “machine learning (ML) algorithms” Response: I have added the abbreviation in the revision (Page 1 line 6-7). 2. Page 1, line 7 Change “SVM” for “Support Vector Machine (SVM)” Response: I have added the abbreviation of “SVM” in the revision (Page 1 line 7). 3. Page 1, line 9 Change “machine learning algorithms” for “ML algorithms” Response: I have used the abbreviation in the revision (Page 1 line 9). 4. Page 1, line 13 Change “IP adresses” for “Internet Protocol (IP) addresses” Response: I have given the full name of IP in the revision (Page 1 line 14). 5. Page 1, line 15 Change “machine learning models” for “ML models” Response: I have used the abbreviation of “ML” in the revision (Page 2 line 15). 6. Page 2, line 18 Change “machine learning methods” for “ML methods” Response: I have used the abbreviation of “ML” in the revision (Page 12 line 18). 7. Page 2, line 27 Change “machine learning algorithms” for “ML algorithms” Response: I have used the abbreviation of “ML” in the revision (Page 2 line 27). 8. Page 2, line 30 “…over time1.” Confirm in the Plos One standards if footnotes can be used. Response: We have not found the standards about the footnotes. However, this standard is used in other journals. So I do no changes and keep the original format in the revision (Page 2 line 30). 9. Page 2, line 39 Change “machine learning” for “ML” Response: I have used the abbreviation of “ML” in the revision (Page 2 line 39). 10. Page 2, line 55 “The system is available at https://github.com/StoneSongLucky/Private Preserving Outlier Behavior Detection” The link is unavailable. Response: The correct links are https://github.com/StoneSongLucky/Private_Preserving_Outlier_Behavior_Detection and https://github.com/Liu-WeiYi/Private_Preserving_Outlier_Behavior_Detection The link in the paper is right and available. 11. Page 2, line 56-62 Delete: “The remainder of the paper is organized as follows. In Section 2, we discuss recent work on user abnormal behavior recommendation. Section 3 describes the whole proposed model, and describes network construction (Section 3.3), network analysis (Section 3.4), outlier recommendation (Section 3.5) and a visualization practical system (Section 3.6). In Section 4, we present a thorough evaluation of the proposed privacy-preserving recommendation system. Finally, we conclude and describe future work in Section 5.” Response: I have deleted those contents in the revision. 12. Page 3, line 65 Change “machine learning” for “ML” Response: I have used the abbreviation of “ML” to replace ‘machine learning’’ in the revision (Page 2 line 57). 13. Page 3, line 71 Change “machine learning (ML) models” for “ML models” - The abbreviation should appear the first time the word is cited in the manuscript (as per the previous changes I am suggesting). Response: I have revised those mistakes in the revision (Page 3 line 64). 14. Page 10, line 262 Change “machine learning-based algorithms” for “ML-based algorithms” Response: I have used the abbreviation of “ML” in the revision (Page 10 line 254). 15. Page 10, line 266 Change “machine learning-based algorithms” for “ML-based algorithms” Response: I have used the abbreviation of “ML” in the revision (Page 10 line 257). 16. Page 11, line 283 Change “machine learning-based algorithms” for “ML-based algorithms” Response: I have used the abbreviation of “ML” in the revision (Page 11 line 274). Attachments Attachment Submitted filename: Response to Reviewers.pdf https://doi.org/10.1371/journal.pone.0224684.r002
21 Oct 2019 Decision Letter - He Debiao, Editor Privacy-Preserving Recommendation of User Abnormal Behavior Under Multilayer Network PONE-D-19-18509R1 Dear Dr. liu, We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements. Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication. Shortly after the formal acceptance letter is sent, an invoice for payment will follow. To ensure an efficient production and billing process, please log into Editorial Manager at https://www.editorialmanager.com/pone/, click the "Update My Information" link at the top of the page, and update your user information. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, you must inform our press team as soon as possible and no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. With kind regards, He Debiao Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #1: All comments have been addressed Reviewer #2: All comments have been addressed ******** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: I Don't Know Reviewer #2: Yes ****** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #2: Yes ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes ****** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: Better in the current form since all questions and comment has been addressed. Also, to the best of my understanding comply with Plos One policy. From basic research writing and reporting, the paper has improved significantly. Assessment on the technical aspect is left for other reviewers and the editorial board. Reviewer #2: After the modifications, consider the appropriate manuscript for publication in Plos One in its current form. ****** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review?** For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: Yes: MKA Abdulrahman Reviewer #2: No https://doi.org/10.1371/journal.pone.0224684.r003
Formally Accepted
13 Nov 2019 Acceptance Letter - He Debiao, Editor PONE-D-19-18509R1 Privacy-Preserving Recommendation of User Abnormal Behavior Under Multilayer Network Dear Dr. Liu: I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org. For any other questions or concerns, please email plosone@plos.org. Thank you for submitting your work to PLOS ONE. With kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. He Debiao Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0224684.r004

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