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Advances in Computer, Communication and Computational Sciences pp 35–44Cite as

A Comparative Analysis of Malware Anomaly Detection

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Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1158)

Abstract

We propose a classification model with various machine learning algorithms to adequately recognise malware files and clean (not malware-affected) files with an objective to minimise the number of false positives. Malware anomaly detection systems are the system security component that monitors network and framework activities for malicious movements. It is becoming an essential component to keep data framework protected with high reliability. The objective of malware inconsistency recognition is to demonstrate common applications perceiving attacks through failure impacts. In this paper, we present machine learning strategies for malware location to distinguish normal and harmful activities on the system. This malware data analytics process carried out using the WEKA tool on the figshare dataset using the four most successful algorithms on the preprocessed dataset through cross-validation. Garrett’s Ranking Strategy has been used to rank various classifiers on their performance level. The results suggest that Instance-Based Learner (IBK) classification approach is the most successful.

Keywords

  • Anomaly
  • Malware
  • Data mining
  • Machine learning
  • Detection
  • Analysis

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  • DOI: 10.1007/978-981-15-4409-5_3
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Author information

Authors and Affiliations

  1. School of Computing, Information and Mathematical Sciences, The University of the South Pacific, Suva, Fiji

    Priynka Sharma, Kaylash Chaudhary, Michael Wagner & M. G. M. Khan

Authors
  1. Priynka Sharma
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  2. Kaylash Chaudhary
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  3. Michael Wagner
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  4. M. G. M. Khan
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Corresponding author

Correspondence to Priynka Sharma .

Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Science, University of Missouri–St. Louis, Chesterfield, MO, USA

    Prof. Sanjiv K. Bhatia

  2. Computer Science Engineering Department, ABES Engineering College, Ghaziabad, Uttar Pradesh, India

    Dr. Shailesh Tiwari

  3. Shanghai Advanced Research Institute, Pudong, China

    Dr. Su Ruidan

  4. National Institute of Technology Agartala, Agartala, Tripura, India

    Dr. Munesh Chandra Trivedi

  5. Computer Science Engineering Department, Motilal Nehru National Institute of Technology, Allahabad, Uttar Pradesh, India

    Dr. K. K. Mishra

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Sharma, P., Chaudhary, K., Wagner, M., Khan, M.G.M. (2021). A Comparative Analysis of Malware Anomaly Detection. In: Bhatia, S.K., Tiwari, S., Ruidan, S., Trivedi, M.C., Mishra, K.K. (eds) Advances in Computer, Communication and Computational Sciences. Advances in Intelligent Systems and Computing, vol 1158. Springer, Singapore. https://doi.org/10.1007/978-981-15-4409-5_3

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