abawajy-featuresubset-inpress-2021.pdf (474.63 kB)
Feature Subset Selection for Malware Detection in Smart IoT Platforms
journal contribution
posted on 2021-02-16, 00:00 authored by Jemal AbawajyJemal Abawajy, Abdulbasit Darem, Asma A AlhashmiMalicious software (“malware”) has become one of the serious cybersecurity issues in Android ecosystem. Given the fast evolution of Android malware releases, it is practically not feasible to manually detect malware apps in the Android ecosystem. As a result, machine learning has become a fledgling approach for malware detection. Since machine learning performance is largely influenced by the availability of high quality and relevant features, feature selection approaches play key role in machine learning based detection of malware. In this paper, we formulate the feature selection problem as a quadratic programming problem and analyse how commonly used filter-based feature selection methods work with emphases on Android malware detection. We compare and contrast several feature selection methods along several factors including the composition of relevant features selected. We empirically evaluate the predictive accuracy of the feature subset selection algorithms and compare their predictive accuracy and the execution time using several learning algorithms. The results of the experiments confirm that feature selection is necessary for improving accuracy of the learning models as well decreasing the run time. The results also show that the performance of the feature selection algorithms vary from one learning algorithm to another and no one feature selection approach performs better than the other approaches all the time.
History
Journal
SensorsVolume
21Issue
4Article number
1374Pagination
1 - 19Publisher
MDPILocation
Basel, SwitzerlandPublisher DOI
Link to full text
eISSN
1424-8220Language
engPublication classification
C1 Refereed article in a scholarly journalUsage metrics
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Keywords
Internet of ThingsmalwareAndroid OSfeature selectionmachine learningfilter methodsmalware detectionsmartphonesScience & TechnologyPhysical SciencesTechnologyChemistry, AnalyticalEngineering, Electrical & ElectronicInstruments & InstrumentationChemistryEngineeringPERMISSION-INDUCED RISKDETECTION SYSTEMBEHAVIORTHREATSDistributed ComputingEcology
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