Applying Bayesian probability for Android malware detection using permission features
| Autor: | Mohd Nizam Mohmad Kahar, Juliza Mohamad Arif, Azlee Zabidi, Sharfah Ratibah Tuan Mat, Mohd Faizal Ab Razak |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Software_OPERATINGSYSTEMS
Computer science Permission Static analysis computer.software_genre Computer security ComputingMilieux_MANAGEMENTOFCOMPUTINGANDINFORMATIONSYSTEMS Naive Bayes classifier Statistical classification ComputingMethodologies_PATTERNRECOGNITION Malware Mobile technology Android (operating system) Classifier (UML) computer |
| Zdroj: | 2021 International Conference on Software Engineering & Computer Systems and 4th International Conference on Computational Science and Information Management (ICSECS-ICOCSIM). |
| Popis: | The tremendous rise of mobile technology has boosted malware and has raised the threat of malware. The proliferation of malware has given a great concern among mobile users. Various approaches have been applied to prevent malware spread, including firewalls, antivirus software and many more methods. Google has provided permission features as the main security to filter out the possibility of malware-infected Android mobile. Nevertheless, some permissions immediately granted by Android without user confirmation. This paper proposes a malware detection system based on permission features using Bayesian probability to battle the malware issue. This study used 96,074 samples retrieved from Androzoo and Drebin. By using static analysis, this study focuses on permission features that are significant in Android applications. The experiments conducted using chi-square as an algorithm and Naive Bayes as a classifier. The accuracy of the detection is 85%. In conclusion, the detection of Android malware using the dataset has produced a good performance. |
| Databáze: | OpenAIRE |
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