Efficient Cybersecurity Assessment Using SVM and Fuzzy Evidential Reasoning for Resilient Infrastructure

TL;DR

This paper proposes an integrated security assessment framework combining SVM and fuzzy evidential reasoning to improve cybersecurity evaluation of infrastructure, enabling faster and more systematic risk analysis.

Contribution

It introduces a novel hybrid model that uses SVM for encryption assessment and fuzzy ER for handling uncertainty in security data analysis.

Findings

The model achieves high accuracy, recall, and F1 scores in security assessment.

It enables systematic processing of risk data across multiple security components.

The approach improves speed and reliability of cybersecurity evaluations.

Abstract

With current advancement in hybermedia knowledges, the privacy of digital information has developed a critical problem. To overawed the susceptibilities of present security protocols, scholars tend to focus mainly on efforts on alternation of current protocols. Over past decade, various proposed encoding models have been shown insecurity, leading to main threats against significant data. Utilizing the suitable encryption model is very vital means of guard against various such, but algorithm is selected based on the dependency of data which need to be secured. Moreover, testing potentiality of the security assessment one by one to identify the best choice can take a vital time for processing. For faster and precisive identification of assessment algorithm, we suggest a security phase exposure model for cipher encryption technique by invoking Support Vector Machine (SVM). In this work, we form a dataset using usual security components like contrast, homogeneity. To overcome the uncertainty in analysing the security and lack of ability of processing data to a risk assessment mechanism. To overcome with such complications, this paper proposes an assessment model for security issues using fuzzy evidential reasoning (ER) approaches. Significantly, the model can be utilised to process and assemble risk assessment data on various aspects in systematic ways. To estimate the performance of our framework, we have various analyses like, recall, F1 score and accuracy.