Network vulnerability Prediction by using the SVM Technique

Masood Ahmad; Malik Shahzad Ahmad; Priyanka S Talekar; Afsaruddin; Mohd Faizan; Mohd Warish Khan

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Network vulnerability Prediction by using the SVM Technique

Author(s)

Masood Ahmad Department of Computer Engineering & application, GLA University,Mathura,India

Malik Shahzad Ahmad Department of Computer Science & Engineering, Acharya University,,Uzbekistan

Priyanka S Talekar Department of Artificial Intelligence and Machine Learning, CMR Institute of Technology,Bangalore, Karnataka,India

Afsaruddin Department of Computer Engineering & application, GLA University,Mathura,India

Mohd Faizan Department of Computer Application, Integral University Lucknow,,

Mohd Warish Khan Department of Computer Application, Integral University Lucknow,,

Abstract

The increasing use of healthcare devices and their communication networks has raised concerns about the security of patient information and the potential for cyber-attacks. This study proposes a machine learning (ML) approach to classify security vulnerabilities in healthcare device communication networks. We collected a dataset of vulnerabilities specific to healthcare devices and applied feature selection and engineering techniques to identify the most relevant features for the classification task. In this paper authors applied the ML techniques and trained several machine learning algorithms, including the Snort algorithm and support vector machines (SVM), to evaluate their effectiveness in this context. We assessed the performance of these models using various evaluation metrics, focusing on accuracy, precision, recall, and F1-score. The results demonstrated that both the SVM and Snort algorithms achieved an accuracy of 94%, a precision of 95%, a recall of 93%, and an F1-score of 94%. These metrics indicate that our ML-based approach is highly effective in identifying security vulnerabilities in healthcare device communication networks. Our approach offers a robust solution for the classification of security vulnerabilities, enabling healthcare providers to identify and prioritize potential threats. This can lead to improved security practices and enhance patient safety by mitigating the risks associated with cyber-attacks on healthcare devices. Our findings suggest that integrating machine learning techniques into security protocols can significantly bolster the defenses of healthcare communication networks.

Publication Details

Page(s) 1392-1403

DOI DOI not available

Published Journal: International Journal of Communication Networks and Information Security, Volume: 16, Issue: 4, Year: 2024

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Keywords

machine learning, Vulnerabilities, Network, SVM, Snort

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