A METHOD FOR ENDPOINT AWARE INSPECTION IN A NETWORK SECURITY SOLUTION

Approved

Positive Comments:

1. Relevance and Originality: The paper tackles a pressing issue in network security, especially relevant given the increase in remote work post-COVID-19. The novel approach to improving decision accuracy using endpoint assessment is both original and valuable.
2. Methodology: The use of hash fingerprints from a subset of the network to enhance endpoint security is innovative and broadly applicable across different security solutions, including NGFWs and SaaS.
3. Validity & Reliability: The proof-of-concept showing a 100% recognition rate indicates the method’s effectiveness and reliability in identifying network affiliations.
4. Clarity and Structure: The paper is well-organized, presenting the problem, solution, and proof-of-concept clearly. The explanation of the method's operation across different network layers is clear and comprehensive.

Negative Comments:

1. Relevance and Originality: The paper could benefit from a comparison with existing solutions to highlight its unique contributions and advantages more clearly.
2. Methodology: Details on the specific techniques for collecting and analyzing hash fingerprints are vague. More information on experimental setup and comparisons with existing methods would be helpful.
3. Validity & Reliability: The proof-of-concept results are promising but lack detailed information on testing conditions and limitations. Additional validation in varied environments would strengthen reliability.
4. Clarity and Structure: The explanation of the method’s application to different network layers is somewhat unclear. More detailed examples or case studies could improve understanding.