Unsupervised Feature Selection for Outlier Detection on Streaming Data to Enhance Network Security
Round 1
Reviewer 1 Report
The authors present a novel algorithm for Unsupervised Feature Selection for Streaming Outlier Detection (UFSSOD) which is proposed to perform unsupervised feature selection for the purpose of outlier detection on streaming data. The algorithm determines the amount of top-performing features by clustering their score values.
The paper presents a wide set of references while the results are presented in numerous figs and tables.
The manuscript will be interesting to the readers of the field.
Note: Please exclude all equations and functions from the text paragraphs and enumerate/cite them e.g.
The y value is given/computed by Equation (1):
y=a*x+b (1)
Author Response
Please see the attachment.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
The Research Paper needs the following Improvements and is Subject for re-review, and after re-review the final decision for the paper will be done:
- Add some Mathematical expressions via System Model of the proposed methodology.
- Add Flowchart of the proposed methodology.
- Add some performance comparison via some Case study based discussion in the paper as compared to existing techniques.
- Add future scope to this paper.
- It is suggested to check the paper for English editing for minor Grammatical Changes and Improvements.
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
