Leveraging machine learning for proactive detection and mitigation of Android RAT

Author:

Ghosh Subhasish,Ponduru Jithin,Thatikonda Mukesh,Mandal Amit Kr

Funder

Science and Engineering Research Board

Publisher

Springer Science and Business Media LLC

Reference42 articles.

1. AhMyth (2020) Ahmyth Android RAT. https://github.com/AhMyth/AhMyth-Android-RAT. Accessed 19 Apr 2023

2. Ani (2023) Apple hack warnings: Mos Rajeev Chandrashekhar rebuts Washington post; says report has “half facts, fully embellished”. The Hindu. https://www.thehindu.com/news/national/apple-hack-warnings-mos-rajeev-chandrashekhar-rebuts-washington-post-says-report-has-half-facts-fully-embellished/article67685918.ece. Accessed 03 Jan 2024

3. Babayeva K (2021) Android mischief dataset. https://data.mendeley.com/datasets/xbx2j63xfd/1

4. Blancaflor E, De Mata KKF, Peralta JC et al (2023) Remote access penetration testing simulation on a mobile phone using Camphish, storm-breaker, & ghost framework. In: 2023 13th International Conference on Software Technology and Engineering (ICSTE). IEEE, pp 20–25. https://doi.org/10.1109/ICSTE61649.2023.00011

5. Bonderud D (2023) Romcom rat attack analysis: fake it to make it. Security Intelligence. https://securityintelligence.com/articles/romcom-rat-attack-analysis/ Accessed 21 May 2023

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