Efficient Cybersecurity Assessment Using SVM and Fuzzy Evidential Reasoning for Resilient Infrastructure
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909665118388224 |
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| author | Ali, Zaydon L. Hayale, Wassan Saad Abduljabbar Al_Barazanchi, Israa Ibraheem Sekhar, Ravi Shah, Pritesh Parihar, Sushma |
| author_facet | Ali, Zaydon L. Hayale, Wassan Saad Abduljabbar Al_Barazanchi, Israa Ibraheem Sekhar, Ravi Shah, Pritesh Parihar, Sushma |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_22938 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Efficient Cybersecurity Assessment Using SVM and Fuzzy Evidential Reasoning for Resilient Infrastructure Ali, Zaydon L. Hayale, Wassan Saad Abduljabbar Al_Barazanchi, Israa Ibraheem Sekhar, Ravi Shah, Pritesh Parihar, Sushma Cryptography and Security Machine Learning 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. |
| title | Efficient Cybersecurity Assessment Using SVM and Fuzzy Evidential Reasoning for Resilient Infrastructure |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2506.22938 |