Malware detection in android mobile platform using machine learning algorithms

Mariam Al Ali, Davor Svetinovic, Zeyar Aung, Suryani Lukman

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

23 Scopus citations

Abstract

Malware has always been a problem in regards to any technological advances in the software world. Thus, it is to be expected that smart phones and other mobile devices are facing the same issues. In this paper, a practical and effective anomaly based malware detection framework is proposed with an emphasis on Android mobile computing platform. A dataset consisting of both benign and malicious applications (apps) were installed on an Android device to analyze the behavioral patterns. We first generate the system metrics (feature vector) from each app by executing it in a controlled environment. Then, a variety of machine learning algorithms: Decision Tree, K Nearest Neighbor, Logistic Regression, Multilayer Perceptron Neural Network, Naive Bayes, Random Forest, and Support Vector Machine are used to classify the app as benign or malware. Each algorithm is assessed using various performance criteria to identify which ones are more suitable to detect malicious software. The results suggest that Random Forest and Support Vector Machine provide the best outcomes thus making them the most effective techniques for malware detection.

Original languageBritish English
Title of host publication2017 International Conference on Infocom Technologies and Unmanned Systems
Subtitle of host publicationTrends and Future Directions, ICTUS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages763-768
Number of pages6
ISBN (Electronic)9781538605141
DOIs
StatePublished - 7 Feb 2018
Event2017 International Conference on Infocom Technologies and Unmanned Systems, ICTUS 2017 - Dubai, United Arab Emirates
Duration: 18 Dec 201720 Dec 2017

Publication series

Name2017 International Conference on Infocom Technologies and Unmanned Systems: Trends and Future Directions, ICTUS 2017
Volume2018-January

Conference

Conference2017 International Conference on Infocom Technologies and Unmanned Systems, ICTUS 2017
Country/TerritoryUnited Arab Emirates
CityDubai
Period18/12/1720/12/17

Keywords

  • Android
  • apps
  • classification
  • machine learning
  • malware detection

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