z-logo
open-access-imgOpen Access
A Survey on Malware Detection Techniques on Linux Powered Smart Phones using Machine Learning Approaches
Author(s) -
Rahul Pawar,
C. Mahesh
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i3.34.18706
Subject(s) - malware , anomaly detection , computer science , pace , computer security , smart phone , harm , constant false alarm rate , data mining , artificial intelligence , telecommunications , geodesy , political science , law , geography
Mobile Phone manufacturers are continuously working to take move on with rapid pace on their new models and to match with the need of customer, they need to customize their system. However the security scenarios of such practice are not that known, due to this various malware and viruses are increasing day by day and causing harm to the devices. Due to the substantial damage caused by malware in last few years certain significant efforts on developing detection and defense mechanism against malwares. For detecting such malicious applications and malwares a security system should be developed which will target such anomaly or outliers in system. In data mining anomaly detection system plays a major role by monitoring the behavior of an application and categorizing them in to normal and abnormal to detect malwares present in the system.  

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here