abstract

Comparing Performance of Malware Classification on Automated Stacking

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Yu-Wen ChenAuthors Info & Claims

ACMSE '20: Proceedings of the 2020 ACM Southeast Conference

Pages 307 - 308

https://doi.org/10.1145/3374135.3385316

Published: 25 May 2020 Publication History

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Abstract

Stacking in machine learning allows multiple classification or regression algorithms to work together with a goal to enhance performance. To understand the risky properties of malware contamination in a system, it is important to accurately classify malware type first. Malware classification is the procedure of labeling the families of malware. In this paper, we automate stacking with 7 machine learning algorithms and 3 boosting algorithms. The experimental results show a 99.2% accuracy is achieved from a multilayer perceptron network with AdaBoost classifier, which outperforms other models on the malware API call dataset.

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Maniriho PMahmood AChowdhury M(2022)A study on malicious software behaviour analysis and detection techniquesFuture Generation Computer Systems10.1016/j.future.2021.11.030130:C(1-18)Online publication date: 1-May-2022
https://dl.acm.org/doi/10.1016/j.future.2021.11.030
Bertoni MRosa GBrega J(2021)Optimum-path forest stacking-based ensemble for intrusion detectionEvolutionary Intelligence10.1007/s12065-021-00609-715:3(2037-2054)Online publication date: 12-May-2021
https://doi.org/10.1007/s12065-021-00609-7
Maniriho PMahmood AChowdhury M(undefined)Evaluation and Survey of State of the Art Malware Detection and Classification Techniques: Analysis and RecommendationSSRN Electronic Journal10.2139/ssrn.4197678
https://doi.org/10.2139/ssrn.4197678

Index Terms

Comparing Performance of Malware Classification on Automated Stacking
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
      1. Ensemble methods
        Boosting
2. Security and privacy
  1. Intrusion/anomaly detection and malware mitigation

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Published In

ACMSE '20: Proceedings of the 2020 ACM Southeast Conference

April 2020

337 pages

ISBN:9781450371056

DOI:10.1145/3374135

Conference Chair:
Morris Chang,
Program Chair:
Dan Lo,
Publications Chair:
Eric Gamess

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 25 May 2020

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ACM SE '20: 2020 ACM Southeast Conference

April 2 - 4, 2020

FL, Tampa, USA

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Overall Acceptance Rate 502 of 1,023 submissions, 49%

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Maniriho PMahmood AChowdhury M(2022)A study on malicious software behaviour analysis and detection techniquesFuture Generation Computer Systems10.1016/j.future.2021.11.030130:C(1-18)Online publication date: 1-May-2022
https://dl.acm.org/doi/10.1016/j.future.2021.11.030
Bertoni MRosa GBrega J(2021)Optimum-path forest stacking-based ensemble for intrusion detectionEvolutionary Intelligence10.1007/s12065-021-00609-715:3(2037-2054)Online publication date: 12-May-2021
https://doi.org/10.1007/s12065-021-00609-7
Maniriho PMahmood AChowdhury M(undefined)Evaluation and Survey of State of the Art Malware Detection and Classification Techniques: Analysis and RecommendationSSRN Electronic Journal10.2139/ssrn.4197678
https://doi.org/10.2139/ssrn.4197678

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Deep learning models for multi-class malware classification using Windows exe API calls

Malware Function Classification Using APIs in Initial Behavior

AdaBoost classifiers for pecan defect classification

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