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Quantifying the impact of adversarial evasion attacks on machine learning based android malware classifiers

Research article (2017 IEEE 16th International Symposium on Network Computing and Applications (NCA), 2017) · cited 33× · AI/ML
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Summary

Quantifying the impact of adversarial evasion attacks on machine learning based android malware classifiers is a scholarly article[1].

Key Facts

  • Quantifying the impact of adversarial evasion attacks on machine learning based android malware classifiers's instance of is recorded as scholarly article[2].

Related Entities

Mohamed Ali Kaafar

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Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

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  1. [1] ↑ . Wikidata. wikidata.org.

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APA 4ort.xyz Knowledge Graph. (2026). Quantifying the impact of adversarial evasion attacks on machine learning based android malware classifiers. Retrieved October 18, 2026, from https://4ort.xyz/entity/quantifying-the-impact-of-adversarial-evasion-attacks-on-machine-learning-based-android-malware-classifiers
MLA “Quantifying the impact of adversarial evasion attacks on machine learning based android malware classifiers.” 4ort.xyz Knowledge Graph, 4ort.xyz, 18 Oct. 2026, https://4ort.xyz/entity/quantifying-the-impact-of-adversarial-evasion-attacks-on-machine-learning-based-android-malware-classifiers.
BibTeX @misc{4ortxyz_quantifying-the-impact-of-adversarial-evasion-attacks-on-machine-learning-based-android-malware-classifiers_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Quantifying the impact of adversarial evasion attacks on machine learning based android malware classifiers}}, year = {2026}, url = {https://4ort.xyz/entity/quantifying-the-impact-of-adversarial-evasion-attacks-on-machine-learning-based-android-malware-classifiers}, note = {Accessed: 2026-10-18}}
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