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  • Large-Scale Malicious Software Classification with Fuzzified Features and Boosted Fuzzy Random Forest

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    Accepted Manuscript (AM)
    Author(s)
    Li, Fangqi
    Wang, Shilin
    Liew, Alan Wee-Chung
    Ding, Weiping
    Liu, Gong Shen
    Griffith University Author(s)
    Liew, Alan Wee-Chung
    Year published
    2020
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    Abstract
    Classification of malicious software, especially in a very large dataset, is a challenging task for machine intelligence. Malware can have highly diversified features, each of which has highly heterogeneous distributions. These factors increase the difficulties for traditional data analytic approaches to deal with them. Although deep learning-based methods have reported good classification performance, the deep models usually lack interpretability and are fragile under adversarial attacks. To solve these problems, fuzzy systems have become a competitive candidate in malware analysis. In this paper, a new fuzzy-based approach ...
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    Classification of malicious software, especially in a very large dataset, is a challenging task for machine intelligence. Malware can have highly diversified features, each of which has highly heterogeneous distributions. These factors increase the difficulties for traditional data analytic approaches to deal with them. Although deep learning-based methods have reported good classification performance, the deep models usually lack interpretability and are fragile under adversarial attacks. To solve these problems, fuzzy systems have become a competitive candidate in malware analysis. In this paper, a new fuzzy-based approach is proposed for malware classification. We focused on portable executable files in the Windows platform and analyzed the distributions of static features and content-oriented features. Fuzzification was used to reduce the ubiquitous impact of noise and outliers in a very large dataset. Finally, a novel boosted classifier consisted of fuzzy decision trees and support vector machine is proposed to perform the malware classification. By using fuzzy decision trees, the inner structure of the classifier can be readily interpreted as discriminative rules, while the novel boosting strategy provides state-of-the-art classification performance. Extensive experimental results showed that our method significantly outperformed several state-of-the-art classifiers.
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    Journal Title
    IEEE Transactions on Fuzzy Systems
    DOI
    https://doi.org/10.1109/tfuzz.2020.3016023
    Copyright Statement
    © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Note
    This publication has been entered in Griffith Research Online as an advanced online version.
    Subject
    Artificial intelligence
    Applied mathematics
    Publication URI
    http://hdl.handle.net/10072/397170
    Collection
    • Journal articles

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