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research-article

Detection and defense of network virus using data mining technology

Published: 18 July 2021 Publication History

Abstract

The spread of network viruses has posed a serious threat to the security of the network; therefore, it is necessary to detect and defend them effectively. This paper used debugging application programming interface (API) technology to obtain the features of API calls as viruses, filtered API calls according to information entropy, and finally used the support vector machine (SVM) model for virus detection. The experimental results showed that when the number of API was 1200, the algorithm had the best virus detection performance, with an average true positive rate (TPR) of 95.2%, a false positive rate (FPR) of 3.31%, and an overall accuracy of 95.42%; compared with the C4.5 algorithm, the K‐means algorithm, and the Naive Bayes algorithm, the SVM algorithm had the best performance. The results show that the proposed method is effective in virus detection and defense and can be further promoted and applied in practice.

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Information & Contributors

Information

Published In

cover image Security and Privacy
Security and Privacy  Volume 4, Issue 6
November/December 2021
135 pages
EISSN:2475-6725
DOI:10.1002/spy2.v4.6
Issue’s Table of Contents

Publisher

John Wiley & Sons, Inc.

United States

Publication History

Published: 18 July 2021

Author Tags

  1. application programming interface call
  2. data mining
  3. feature selection
  4. network virus
  5. virus detection

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