FNN and Auto Encoder Deep Learning-Based Algorithm for Android Cyber Security
Seema Vanjire1, M. Lakshmi2
1Seema Vanjire, Assistant Professor, Sinhgad Academy of Engineering, Kondhwa Pune, India.
2Dr. M. Lakshmi, Professor, Department of CSE, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Kuthambakkam, Tamil Nadu, India.
Manuscript received on January 01, 2020. | Revised Manuscript received on January 20, 2020. | Manuscript published on January 30, 2020. | PP: 3292-3296 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6454018520/2020©BEIESP | DOI: 10.35940/ijrte.E6454.018520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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 Feed Forward Deep Neural Networks) and Auto Encoder 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 Feed Forward Deep Neural Networks) and Auto Encoder approach for android malware detection.
Keywords: Auto Encoder, Android Cyber Security, Deep Learning.
Scope of the Article: Deep Learning.