Representing string computations as graphs for classifying malware-Reference-Cited by-同舟云学术

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Representing string computations as graphs for classifying malware

Published:2020-07-13 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the IEEE/ACM 7th International Conference on Mobile Software Engineering and Systems
language:
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Author:

Vecchio Justin Del¹,Ko Steven Y.¹,Ziarek Lukasz¹

Affiliation:

1. University at Buffalo

Funder

National Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3387905.3388595

Reference56 articles.

1. 2019. Mobile Malware Analysis: Tricks used in Anubis. https://eybisi.run/Mobile-Malware-Analysis-Tricks-used-in-Anubis/. (2019). 2019. Mobile Malware Analysis: Tricks used in Anubis. https://eybisi.run/Mobile-Malware-Analysis-Tricks-used-in-Anubis/. (2019).

2. Kevin Allix Tegawendé F. Bissyandé Jacques Klein and Yves Le Traon. 2016. AndroZoo. (2016) 468--471. 10.1145/2901739.2903508 Kevin Allix Tegawendé F. Bissyandé Jacques Klein and Yves Le Traon. 2016. AndroZoo. (2016) 468--471. 10.1145/2901739.2903508

3. Yassine Alouini. 2019. Why is XGBoost among most used machine learning method on Kaggle?. https://www.quora.com/Why-is-XGBoost-among-most-used-machine-learning-method-on-Kaggle. (2019). Yassine Alouini. 2019. Why is XGBoost among most used machine learning method on Kaggle?. https://www.quora.com/Why-is-XGBoost-among-most-used-machine-learning-method-on-Kaggle. (2019).

4. Hybrid Analysis. 2018. https://www.hybrid-analysis.com. Hybrid Analysis. 2018. https://www.hybrid-analysis.com.

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