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An ensemble approach for imbalanced multiclass malware classification using 1D-CNN.

Binayak Panda1, Sudhanshu Shekhar Bisoyi2, Sidhanta Panigrahy3

  • 1Department of Computer Science and Engineering, Institute of Technical Education and Research, Siksha 'O' Anusandhan (Deemed to be) University, Bhubaneswar, Odisha, India.

Peerj. Computer Science
|December 11, 2023
PubMed
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This study introduces an enhanced deep learning model for malware classification, improving detection accuracy against sophisticated threats. The novel approach effectively categorizes malware variants using application programming interface (API) call sequences.

Area of Science:

  • Computer Science
  • Cybersecurity
  • Artificial Intelligence

Background:

  • Malware developers increasingly use code obfuscation, creating polymorphic and metamorphic variants that evade traditional signature-based detection.
  • The growing volume and variety of malware necessitate advanced analytical techniques beyond conventional machine learning.
  • Classifying malware internally, alongside distinguishing it from benign software, is crucial for understanding behavioral nuances.

Purpose of the Study:

  • To investigate the relationship between application programming interface (API) call sequences for malware classification.
  • To develop and evaluate a deep learning model for multiclass malware categorization based on API call patterns.

Main Methods:

  • Utilized the one-dimensional convolutional neural network (1D-CNN) model for multiclass classification of API sequences.
Keywords:
1D-CNNAPI sequenceDynamic analysisEnsemble learningMalware classificationSkip-gram

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  • Employed Word2Vec and the skip-gram model to generate feature vectors for distinct APIs.
  • Implemented a one-vs.-rest approach for training 1D-CNN models, combined with a ModifiedSoftVoting algorithm for ensemble classification.

    • Main Results:

    • The proposed ensembled 1D-CNN architecture achieved high performance on the Mal-API-2019 dataset.
    • Achieved an accuracy of 0.90, a weighted average F1-score of 0.90, and an AUC score exceeding 0.96 for all malware classes.

    Conclusions:

    • The ensembled 1D-CNN model demonstrates significant effectiveness in classifying diverse malware types based on API call sequences.
    • This deep learning approach offers a robust solution for detecting advanced malware, outperforming existing methods.
    • The findings highlight the potential of deep learning for enhancing cybersecurity defenses against evolving malware threats.