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Deep learning vs. adversarial noise: a battle in malware image analysis

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Abstract

The proliferation of malware variants has shown a steep increase, attributed to their enhanced sophistication and the utilization of the latest technologies. This constitutes a severe menace to smart gadgets and IT infrastructure. Malware visualization has emerged as an exceptionally attractive technique, primarily because it obviates the need for disassembly or code execution. In this approach, malicious executables are transformed into visual representations resembling images. This visual representation allows for the extraction of textural features using the Local Binary Pattern (LBP) technique. Subsequently, classification models are constructed using ResNet50, VGG16, and customized models tailored to the specific task. These model undergoes extensive evaluation through two benchmark datasets: the MalImg dataset (consisting of 9,342 instances of malware across 25 families) and the Malware Classification Challenge dataset (BIG2015) (with 10,868 labeled malware instances across nine families). Additionally, the model is validated on a self-made dataset, which we named Malhub, consisting of 26,452 executables comprising 20 families. Furthermore, we implemented a white-box adversarial attack using additive noise (Gaussian, Local Variable, Poisson, Salt and Pepper, Speckle). We observed an F1 score in the range of 0.992\(-\)0.993 for MalImg, 0.874\(-\)0.878 for BIG2015, and 0.014\(-\)0.992 for Malhub dataset. This proves that efforts are required to tune machine learning models to detect adversarial examples.

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Notes

  1. Cyber Security Trends Report (2021): https://purplesec.us/cyber-security-trends-2021.

  2. Crowdstrike global threat report (2021): https://go.crowdstrike.com/rs/281-OBQ-266/images/ Report2021GTR.pdf.

  3. Global industry sectors most targeted by malware incidents in 2020: https://www.statista.com/statistics/223517/ malware-infection-weekly-industries/

  4. VirusShare: https://virusshare.com/

  5. scikit-image: https://scikit-image.org/

  6. https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html

  7. https://keras.io/keras_tuner/

  8. Github link: https://github.com/OPTIMA-CTI/DL-Adversarial-Noise

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Acknowledgements

We gratefully acknowledge the support provided by the HORIZON Europe Framework Programme for the project “OPTIMA - Organization-specific Threat Intelligence Mining and Sharing" (No. 101063107).

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Authors and Affiliations

  1. Department of Computer Applications, Cochin University of Science & Technology, Kochi, 682022, Kerala, India

    K. A. Asmitha, Vinod Puthuvath & K. A. Rafidha Rehiman

  2. Department of Mathematics, University of Padua, Padua, Italy

    Vinod Puthuvath

  3. Cisco Systems India Pvt. Ltd., Bangalore, India

    S. L. Ananth

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  1. K. A. Asmitha
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  2. Vinod Puthuvath
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Contributions

KAA: software, validation, investigation, data curation, writing—original draft, writing—review and editing. VP: conceptualization, investigation, supervision, writing—original draft, writing—review and editing. KARR: supervision, writing—original draft, writing—review and editing. SLA: software, validation, investigation, writing—original draft, writing—review and editing.

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Correspondence to Vinod Puthuvath.

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Supplementary file 1 (pdf 2196 KB)

Appendix A

Appendix A

This section presents the detailed evaluation results of ML classifiers and Dense (2-Layers). Additionally, we include the ROC curve for the proposed classification model.

Table 15 ML classifiers and Dense(2-layers) performance using LBP images VGG16 as pre-trained network (size \(=\) 128 x 128)
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Table 16 Performance of ML classifiers and Dense(2-layers)using LBP images ResNet50 as pretrained network (size \(=\) 128 x 128)
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Table 17 Evaluation results of ML classifiers and Dense(2-layers) using Non-LBP images (size \(=\) 128 x 128) using pretrained VGG16
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Table 18 Assesment of ML classifiers and Dense(2-layers) using Non-LBP images (size \(=\) 128 x 128) using pretrained ResNet50
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See Figs. 18, 19, and 20.

Fig. 18
figure 18

ROC curve for BIG2015 dataset using proposed fusion model(ResNet50||CNN||DL)

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Fig. 19
figure 19

ROC curve for MalImg dataset using proposed fusion model(ResNet50||CNN||DL)

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Fig. 20
figure 20

ROC curve for Malhub dataset using proposed fusion model(ResNet50||CNN||DL)

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Asmitha, K.A., Puthuvath, V., Rafidha Rehiman, K.A. et al. Deep learning vs. adversarial noise: a battle in malware image analysis. Cluster Comput 27, 9191–9220 (2024). https://doi.org/10.1007/s10586-024-04397-4

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