Open AccessFNN and Auto Encoder Deep Learning-Based Algorithm for Android Cyber Security
Author(s) -
Seema Sachin Vanjire,
M. Lakshmi
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.e6454.018520
Subject(s) - android (operating system) , malware , computer science , deep learning , autoencoder , computer security , android malware , artificial intelligence , mobile malware , mobile device , artificial neural network , machine learning , operating system
Android is susceptible to malware attacks due to its open architecture, large user base and access to its code. Mobile or android malware attacks are increasing from last year. These are common threats for every internet-accessible device. From Researchers Point of view 50% increase in cyber-attacks targeting Android Mobile phones since last year. Malware attackers increasingly turning their attention to attacking smartphones with credential-theft, surveillance, and malicious advertising. Security investigation in the android mobile system has relied on analysis for malware or threat detection using binary samples or system calls with behavior profile for malicious applications is generated and then analyzed. The resulting report is then used to detect android application malware or threats using manual features. To dispose of malicious applications in the mobile device, we propose an Android malware detection system using deep learning techniques which gives security for mobile or android. FNN(Fully-connected FeedForward Deep Neural Networks) and AutoEncoder algorithm from deep learning provide Extensive experiments on a real-world dataset that reaches to an accuracy of 95 %. These papers explain Deep learning FNN(Fully-connected FeedForward Deep Neural Networks) and AutoEncoder approach for android malware detection.