An Improved Binary Owl Feature Selection in the Context of Android Malware Detection

• DOI: 10.3390/computers11120173
• Publication Date: 2022-12-01T06:54:31Z
• Authors: Hadeel Alazzam; Aryaf Al-Adwan; Orieb Abualghanam; Esra’a Alhenawi; Abdulsalam Alsmady
• Keywords: DREBIN dataset; binary owl optimizer; Electronic computers. Computer science; 0202 electrical engineering, electronic engineering, information engineering; QA75.5-76.95; 02 engineering and technology; Android malware detection; binary owl optimizer; DREBIN dataset; Android malware detection

Recently, the proliferation of smartphones, tablets, and smartwatches has raised security concerns from researchers. Android-based mobile devices are considered a dominant operating system. The open-source nature of this platform makes it a good target for malware attacks that result in both data exfiltration and property loss. To handle the security issues of mobile malware attacks, researchers proposed novel algorithms and detection approaches. However, there is no standard dataset used by researchers to make a fair evaluation. Most of the research datasets were collected from the Play Store or collected randomly from public datasets such as the DREBIN dataset. In this paper, a wrapper-based approach for Android malware detection has been proposed. The proposed wrapper consists of a newly modified binary Owl optimizer and a random forest classifier. The proposed approach was evaluated using standard data splits given by the DREBIN dataset in terms of accuracy, precision, recall, false-positive rate, and F1-score. The proposed approach reaches 98.84% and 86.34% for accuracy and F-score, respectively. Furthermore, it outperforms several related approaches from the literature in terms of accuracy, precision, and recall.