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Using Four Modalities for Malware Detection Based on Feature Level and Decision Level Fusion

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Advanced Information Networking and Applications (AINA 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1151))

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Abstract

This paper is focused on multimodal approaches to malware detection, which have not been explored widely in related works. We use static code-based features and dynamic power-based, network traffic-based, and system log-based features, and propose multimodal approaches that use feature level and decision level fusion. Our findings include: (1) For all considered learners, power-based features alone were very good predictors; some learners performed well using only network traffic-based features. (2) For most standard supervised learning algorithms, feature level fusion improved all performance metrics. If Recall is the highest priority, Random Forest or J48 with feature level fusion should be selected. (3) The proposed deep neural network with decision level fusion had lower Recall, but higher Precision and (1-FPR) values, which led to comparable F-score and better G-score than the Random Forest with feature level fusion. In addition to improving classification performance, multimodal approaches make malware evasion of detection much harder.

This work was done while Jarilyn Hernández Jiménez was affiliated with West Virginia University and is included in her Ph.D. dissertation [1].

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Acknowledgments

This work is funded by the National Science Foundation under the grant CNS-1618629.

Author information

Authors and Affiliations

  1. West Virginia University, Morgantown, WV, USA

    Jarilyn M. Hernández Jiménez & Katerina Goseva-Popstojanova

  2. MIT Lincoln Laboratory, Lexington, MA, USA

    Jarilyn M. Hernández Jiménez

Authors
  1. Jarilyn M. Hernández Jiménez
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  2. Katerina Goseva-Popstojanova
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Corresponding author

Correspondence to Jarilyn M. Hernández Jiménez .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Department of Electrical Engineering and Information Technology, University of Naples “Frederico II”, Naples, Italy

    Flora Amato

  3. Department of Political Science, University of Campania “Luigi Vanvitelli”, Caserta, Italy

    Francesco Moscato

  4. Faculty of Business Administration, Rissho University, Tokyo, Japan

    Tomoya Enokido

  5. Department of Advanced Sciences, Faculty of Science and Engineering, Hosei University, Tokyo, Japan

    Makoto Takizawa

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Hernández Jiménez, J.M., Goseva-Popstojanova, K. (2020). Using Four Modalities for Malware Detection Based on Feature Level and Decision Level Fusion. In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds) Advanced Information Networking and Applications. AINA 2020. Advances in Intelligent Systems and Computing, vol 1151. Springer, Cham. https://doi.org/10.1007/978-3-030-44041-1_117

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