Title: HyDroid: android malware detection using network flow combined with permissions and intent filter

Authors: Akram Zine Eddine Boukhamla; Abhishek Verma

Addresses: LINATI Laboratory, Department of Computer Science and Information Technologies, Kasdi Merbah University, Ouargla BP.511,30000, Algeria ' Department of Computer Science and Engineering, School of Engineering, BBD University, Lucknow, Uttar Pradesh, India

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.

Keywords: Android malware detection; static analysis; network flow; hybrid analysis; machine learning.

DOI: 10.1504/IJMC.2023.131799

International Journal of Mobile Communications, 2023 Vol.22 No.1, pp.70 - 91

Received: 09 Apr 2021
Accepted: 11 Jul 2021
Published online: 04 Jul 2023 *

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