Transparent Peer Review By Scholar9

A METHOD FOR ENDPOINT AWARE INSPECTION IN A NETWORK SECURITY SOLUTION

Abstract

Due to the flood in remote work after the episode of Covid, network security has gained a giant fixation. The issue of mixed-up audit decisions in network security plans has for quite a while been reprimanded, but the meaning of the decision precision has never been overall around as critical as today. In this paper we offer a response for additional fostering the assessment decision accuracy by deciding a method for endpoint careful survey in an association security plan prepared for performing significant package examination. The method utilizes a subset of the protected association to gather hash fingerprints from the endpoint application network traffic plans. The information collected from this subset is then utilized for procuring endpoint care for the rest of the protected organization. We use strategies that work on the application layer of the show stack. This makes the strategy fitting not only for neighborhood executions, as NGFWs and IPSs, yet also for SaaS and SASE game plans. The methodology is, regardless, conveniently utilized with lower layer information, for instance, association and transport layer information, for working system care too. We similarly present a proof-of-thought context-oriented examination where that is the thing we see, of the relevant association affiliations, 100% could be recognized while the functioning system and endpoint application were accessible in the source pack. All things considered, this is the primary method to redesign the assessment cycle accuracy by using a subset of the protected association to secure endpoint care.

Pattabi Ramarao Thumati Reviewer

Review Request Accepted

Pattabi Ramarao Thumati Reviewer
27 Aug 2024 09:48 AM

Approved Rating

Relevance and Originality

Methodology

Validity & Reliability

Clarity and Structure

Results and Analysis

Comment

Positive:

This paper presents a timely and relevant approach to enhancing network security decision accuracy, especially pertinent given the rise in remote work following the COVID-19 pandemic. It introduces an innovative method using hash fingerprints from network traffic to improve endpoint security. The technique is versatile, applicable to various security setups including NGFWs, IPSs, SaaS, and SASE, and can handle both application and lower layer data. The proof-of-concept results, showing 100% recognition in relevant scenarios, highlight the effectiveness and potential impact of this method.

Negative:

While the proposed method offers a novel approach, the paper could benefit from a more detailed explanation of the implementation process and its integration into existing security systems. Additionally, the discussion would be strengthened by addressing potential limitations or challenges in applying this method to different network environments. The reliance on a subset of the network for data collection might raise concerns about scalability and generalizability. Further empirical validation and comparison with other security solutions would provide a more comprehensive evaluation of its effectiveness.