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)
We propose a novel android malware detection system that uses a deep convolutional neural network Malware classification is performed based on static analysis of the raw opcode sequence from a disassembled program. To solve these challenges, we propose system to identify malware both efficiently and effectively. The novel technique enabling effectiveness is the Semantic-based deep learning. We use Long Short Term Memory on the semantic structure of Android bytecode, avoiding missing the details of method-level bytecode semantics. To achieve efficiency, we apply Multilayer Perceptron on the xml files based on the finding that most malware can be efficiently identified using information only from xml files.
Keywords:
Android Malware Detection; Deep Learning, Deep Refiner
Cite Article:
"Permission and Header Based Android Application", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.6, Issue 6, page no.98 - 100, June-2021, Available :http://www.ijrti.org/papers/IJRTI2106018.pdf
Downloads:
000204834
ISSN:
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