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• W3118343432 abstract "With the recognition of free apps, Android has become the most widely used smartphone operating system these days and it naturally invited cyber-criminals to build malware-infected apps that can steal vital information from these devices. The most critical problem is to detect malware-infected apps and keep them out of Google play store. The vulnerability lies in the underlying permission model of Android apps. Consequently, it has become the responsibility of the app developers to precisely specify the permissions which are going to be demanded by the apps during their installation and execution time. In this study, we examine the permission-induced risk which begins by giving unnecessary permissions to these Android apps. The experimental work done in this research paper includes the development of an effective malware detection system which helps to determine and investigate the detective influence of numerous well-known and broadly used set of features for malware detection. To select best features from our collected features data set we implement ten distinct feature selection approaches. Further, we developed the malware detection model by utilizing LSSVM (Least Square Support Vector Machine) learning approach connected through three distinct kernel functions i.e., linear, radial basis and polynomial. Experiments were performed by using 2,00,000 distinct Android apps. Empirical result reveals that the model build by utilizing LSSVM with RBF (i.e., radial basis kernel function) named as FSdroid is able to detect 98.8% of malware when compared to distinct anti-virus scanners and also achieved 3% higher detection rate when compared to different frameworks or approaches proposed in the literature." @default.
• W3118343432 created "2021-01-18" @default.
• W3118343432 creator A5036839837 @default.
• W3118343432 creator A5068846958 @default.
• W3118343432 date "2021-01-14" @default.
• W3118343432 modified "2023-10-06" @default.
• W3118343432 title "FSDroid:- A feature selection technique to detect malware from Android using Machine Learning Techniques" @default.
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• W3118343432 doi "https://doi.org/10.1007/s11042-020-10367-w" @default.
• W3118343432 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7807414" @default.
• W3118343432 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33462535" @default.
• W3118343432 hasPublicationYear "2021" @default.
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• W3118343432 citedByCount "32" @default.

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