Open AccessDiscover and Analyzes Whether Mobile Applications Downloaded From the Internet Are Good or Bad
Author(s) -
B. Rajnaveen,
D. Rambabu,
D. J. Naik,
A. Jawahar Babu
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.c5883.118419
Subject(s) - taguchi methods , orthogonal array , corrosion , surface roughness , electrical discharge machining , aluminium , materials science , aluminium alloy , machining , surface finish , mechanical engineering , voltage , grey relational analysis , process variable , fractional factorial design , metallurgy , process (computing) , engineering , composite material , computer science , factorial experiment , electrical engineering , mathematics , operating system , mathematical economics , machine learning
Android Malware is pernicious software. It is configured to attack the hardware such as android or mobile phone or smart phone. It is designed to exploit the flaw in specific mobile phone software technologies and operating systems. Nowadays, the mobile phone is the number one most vulnerable to malware attacks. Malware can be in the form of adware, Trojans, viruses, root kits and spyware. They delete important documents or steal protected data or bring software that is not authorized by the user. To solve this problem you need to categorize the applications on the mobile. The techniques used in machine learning are used here to differentiate between applications in mobile as good or bad. In this paper, present two methods as using the Genetic algorithm for feature selection and the Nearest Neighbor for classification.