Detecting Malicious Apps in Android Devices using SVM, Random Forest & Decision Trees
E. Sanjana1, M. Srikanth Sagar2, Deekshitha Nalla3, Bonthu Meghana4, Anusri Katragadda5
1E. Sanjana , Student, Department of Computer Science and Engineering, Mahatma Gandhi Institute of Technology, Hyderabad, India.
2M. Srikanth Sagar, Assistant professor, Department of Computer Science And Engineering Mahatma Gandhi Institute of Technology, Hyderabad, India.
3Deekshitha Nalla, Student, Department of Computer Science and Engineering, Mahatma Gandhi Institute of Technology, Hyderabad, India.
4Bonthu Meghana, Student, Department of Computer Science and Engineering, Mahatma Gandhi Institute of Technology, Hyderabad, India.
5Anusri Katragadda, Student, Department of Computer Science and Engineering, Mahatma Gandhi Institute of Technology, Hyderabad, India.
Manuscript received on April 30, 2020. | Revised Manuscript received on May 06, 2020. | Manuscript published on May 30, 2020. | PP: 1414-1417 | Volume-9 Issue-1, May 2020. | Retrieval Number: A2418059120/2020©BEIESP | DOI: 10.35940/ijrte.A2418.059120
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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: In recent years, the usages of smart phones are increasing steadily and also growth of Android application users are increasing. Due to growth of Android application user, some intruder are creating malicious android application as tool to steal the sensitive data. We need an effective and efficient malicious applications detection tool to handle new complex malicious apps created by intruder or hackers. This project deals with idea of using machine learning approaches for detecting the malicious android application. First we have to gather dataset of past malicious apps as training set and with the help of Support vector machine algorithm and decision tree algorithm make up comparison with training dataset and trained dataset we can predict the malware android apps upto 93.2 % unknown / New malware mobile application. By implementing SIGPID, Significant Permission Identification (SIGPID).The goal of the sigid is to improve the apps permissions effectively and efficiently. This SIGPID system improves the accuracy and efficient detection of malware application. With the help of machine learning algorithms such as SVM, Random Forest Classifier and Decision Tree algorithms we make a comparison between training dataset and trained dataset to classify malicious application and benign app.
Keywords: SVM(Supprt Vector Macchine), Random Forest Classifier, (SIGPID) Significant Permission Identification, Decision Tree algorithm.
Scope of the Article: Classification