HyDroid: android malware detection using network flow combined with permissions and intent filter Online publication date: Tue, 04-Jul-2023
by Akram Zine Eddine Boukhamla; Abhishek Verma
International Journal of Mobile Communications (IJMC), Vol. 22, No. 1, 2023
Abstract: Android has become one of the most widely used operating systems for mobile platforms in the recent years. With its widespread adoption, it has also became the target of malicious applications' developers and cyber threats. This in turn has stimulated research on android malware analysis and detection. Several android malware detection techniques have been proposed in the literature. In this paper, we propose a novel hybrid android malware detection method which is named as HydDroid. A hybrid dataset based on the existing CICInvesAndMal2019 dataset by selecting most relevant static features is created. HydDroid is represented by the form of a combination of binary vectors and numerical vectors. The proposed approach is evaluated using three well-known machine learning classification algorithms. The experiment results indicate that HydDroid achieves the accuracy of up to 96.3%. To show the effectiveness of our proposed approach, the performance results are compared with existing solutions.
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