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

2025-06-28

AI Summary

To address the low efficiency, high uncertainty, and difficulty in fusing multi-source risk indicators in current cryptographic algorithm security assessments, this paper proposes a cybersecurity evaluation model integrating Support Vector Machines (SVM) and Fuzzy Evidence Reasoning (Fuzzy ER). The model uniquely combines SVM’s strong classification capability with Fuzzy ER’s robust handling of uncertain and imprecise information, establishing a systematic framework for integrating multidimensional security features and heterogeneous risk indicators. Through security feature engineering and risk data fusion analysis, it enables rapid, accurate, and multidimensional evaluation of cryptographic technique security. Experimental results demonstrate that the proposed model significantly outperforms baseline methods in recall, F1-score, and accuracy, while reducing evaluation time substantially. It thus enhances both the reliability of cryptographic algorithm selection and the precision of security decision-making in complex environments.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Cognitive Modeling & Cognitive Systems: Symbolic Representations

Application Category

• Security and Privacy: Applications of cryptography
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• Identifying optimal encryption algorithms efficiently using SVM
• Overcoming uncertainty in security analysis with fuzzy ER
• Enhancing cybersecurity assessment for resilient infrastructure

Innovation

• Uses SVM for faster security algorithm identification
• Applies fuzzy evidential reasoning for risk assessment
• Combines contrast and homogeneity for dataset formation