Robust Android Malware Detection Against Adversarial Attacks-Reference-Cited by-同舟云学术

Robust Android Malware Detection Against Adversarial Attacks

Published:2024 Issue: Volume: Page:585-595
ISSN:2367-3370
Container-title:Lecture Notes in Networks and Systems
language:en
Short-container-title:

Author:

Nikale Swapna Augustine,Purohit Seema

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-99-6547-2_45

Reference11 articles.

1. Bhusal D, Rastogi N (2022) Adversarial patterns: building robust android malware classifiers. Accessed: 06 Apr 2023. [Online]. Available: http://arxiv.org/abs/2203.02121

2. Kamath A, Bhatu V, Paranjape T, Sawant R (2022) Malware classification and defence against adversarial attacks. In: Gupta D, Polkowski Z, Khanna A, Bhattacharyya S, Castillo O (eds) Proceedings of data analytics and management. Lecture notes on data engineering and communications technologies, vol 91. Singapore: Springer Singapore, pp 267–274. https://doi.org/10.1007/978-981-16-6285-0_22

3. Rafiq H, Aslam N, Issac B, Randhawa RH (2022) An investigation on fragility of machine learning classifiers in android malware detection. In: IEEE INFOCOM 2022—IEEE conference on computer communications workshops (INFOCOM WKSHPS), New York, NY, USA, IEEE, May 2022, pp 1–6. https://doi.org/10.1109/INFOCOMWKSHPS54753.2022.9798161

4. Bala N, Ahmar A, Li W, Tovar F, Battu A, Bambarkar P (2022) DroidEnemy: battling adversarial example attacks for android malware detection. Digit Commun Netw 8(6):1040–1047. https://doi.org/10.1016/j.dcan.2021.11.001

5. Li X, Kong K, Xu S, Qin P, He D (2021) Feature selection-based android malware adversarial sample generation and detection method. IET Inf Secur 15(6):401–416. https://doi.org/10.1049/ise2.12030