Optimizing Android Malware Detection Via Ensemble Learning
| Autor: | Abikoye Oluwakemi Christianah, Benjamin Aruwa Gyunka, Akande Noah Oluwatobi |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Reverse engineering Computer Networks and Communications Computer science TK5101-6720 computer.software_genre Machine learning 03 medical and health sciences 0302 clinical medicine Reverse Engineering Android (operating system) Base Learners Ensemble forecasting business.industry Android Malware Detection android malware detection machine learning models base learners ensemble learner reverse engineering Ensemble learning Computer Science Applications Random forest Support vector machine Ensemble Learner 030104 developmental biology Telecommunication Malware Artificial intelligence False positive rate business computer 030217 neurology & neurosurgery Machine Learning Models |
| Zdroj: | International Journal of Interactive Mobile Technologies (iJIM); Vol. 14 No. 09 (2020); pp. 61-78 International Journal of Interactive Mobile Technologies, Vol 14, Iss 09, Pp 61-78 (2020) |
| ISSN: | 1865-7923 |
| DOI: | 10.3991/ijim.v14i09.11548 |
| Popis: | Android operating system has become very popular, with the highest market share, amongst all other mobile operating systems due to its open source nature and users friendliness. This has brought about an uncontrolled rise in malicious applications targeting the Android platform. Emerging trends of Android malware are employing highly sophisticated detection and analysis avoidance techniques such that the traditional signature-based detection methods have become less potent in their ability to detect new and unknown malware. Alternative approaches, such as the Machine learning techniques have taken the lead for timely zero-day anomaly detections. The study aimed at developing an optimized Android malware detection model using ensemble learning technique. Random Forest, Support Vector Machine, and k-Nearest Neighbours were used to develop three distinct base models and their predictive results were further combined using Majority Vote combination function to produce an ensemble model. Reverse engineering procedure was employed to extract static features from large repository of malware samples and benign applications. WEKA 3.8.2 data mining suite was used to perform all the learning experiments. The results showed that Random Forest had a true positive rate of 97.9%, a false positive rate of 1.9% and was able to correctly classify instances with 98%, making it a strong base model. The ensemble model had a true positive rate of 98.1%, false positive rate of 1.8% and was able to correctly classify instances with 98.16%. The finding shows that, although the base learners had good detection results, the ensemble learner produced a better optimized detection model compared with the performances of those of the base learners. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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