Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Android, as the most widespread mobile applications, is increasingly becoming the target of malware. Malicious applications designed to turn mobile devices into bots that may become part of a larger botnet are becoming increasingly common, thus posing a greater risk. This requires the most efficient ways to get the botnet on the Android platform. Therefore, in this project, we are using an in-depth learning botnet for Android botnet detection based on Convolutional Neural Networks (CNN). Our proposed botnet detection system is used as a CNN-based model trained in 342 static application features to distinguish between botnet applications and standard applications.
"Mobile Botnet Sentinel Using CNN", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.7, Issue 5, page no.830 - 836, June-2022, Available :http://www.ijrti.org/papers/IJRTI2205137.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator