An Effective Performance Based on Static and Dynamic Features to Detect Malware in Android by Machine Learning Algorithm
P.S.Rajakumar1, V. R. Niveditha2, T. V. Ananthan3, N. Kanya4
1P.S.Rajakumar *, Professor Department of Computer Science and Engineering. Dr. M.G.R. Educational and Research Institute, Chennai, Tamil Nadu, India.
2V. R. Niveditha, Research Scholar Department of Computer Science and Engineering. Dr. M.G.R. Educational and Research Institute, Chennai, Tamil Nadu, India.
3T.V.Ananthan, Professor, Department of Computer Science an dEngineering. Dr. M.G.R. Educational and Research Institute, Chennai, Tamil Nadu, India.
4N.Kanya, Professor Department of Computer Science and Engineering. Dr. M.G.R. Educational and Research Institute, Chennai, Tamil Nadu, India.
Manuscript received on November 11, 2019. | Revised Manuscript received on November 20 2019. | Manuscript published on 30 November, 2019. | PP: 11240-11243 | Volume-8 Issue-4, November 2019. | Retrieval Number: D9421118419/2019©BEIESP | DOI: 10.35940/ijrte.D9421.118419
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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 dominating the smartphone market with more users than any other mobile operating system. Yet concern from hackers has also risen with its growing popularity, as the number of malicious applications remains to rise. Wide-ranging work on malware identification and prevention for Android devices has been carried out in recent years, although Android has also introduced numerous security measures to fix malware complications, containing Unique User ID (UID) for every request, software permissions, and its Google Play scattering platform. In our proposed work, incorporates the analysis of static and dynamic features of these applications with the goal of evaluating their actions by exploring different attributes such as authorization, use of CPUs and storage utilization. In this paper machine learning methods to evaluate the comparative efficiency of extracted static and dynamic features to identify AM. This results is quite powerful and demonstrates the AUC of 0.972 can be used to identify AM with a high degree of accuracy than the dynamic function.
Keywords: Static Features, Dynamic Features, Machine Learning (ML) Algorithm, Android Malware (AM).
Scope of the Article: Machine Learning.