Android Malware Detection Using Deep Eigenspace Learning
AbstractToday’s open source android smartphone package is adept of executing the multifarious and enormous application that will increase the installation of various applications with increase in probability of installation of malware application. The behaviour pattern of android is represented by the requested permission of application. System explores the simplest way to discover malware application supported requested permission by the application. Detection of malware application is completed in 2 steps; start is to choosing representative features by applying the FAST algorithm. Whereas representative feature is extracted permissions, requested within the application. In second step classification of application is completed as a malware or benign application victimisation support vector machine (SVM). Using fast and SVM algorithms system will discriminate android application as malware conjointly enrich the performance of malware detection system.
How to Cite
KHEDKAR, Akanksha; KUMAR, Golu; DHUMAL, Apurva. Android Malware Detection Using Deep Eigenspace Learning. International Journal Of Emerging Technology and Computer Science, [S.l.], v. 4, n. 2, p. 26-31, may 2019. ISSN 2455-9954. Available at: <https://aspirepublishers.com/index.php/ijetcs/article/view/262>. Date accessed: 31 may 2020.