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Journals / Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi / 2026 / Cilt: 14 Sayı: 1

Hybroid: A Novel Hybrid Android Malware Detection Framework

Abdullah Talha Kabakuş(DÜZCE ÜNİVERSİTESİ)0000-0003-2181-4292
Pages
331–356
Publication Date
2021-03-31
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The key findings in consequence of the experimental result are discussed in order to shed light on Android malware detection.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Android, the most widely-used mobile operating system, attracts the attention of malware developers as well as benign users.
📊
Key Findings & Data:Android malware detection systems are generally classified into two: (1) Static analysis, and (2) dynamic analysis.
💡
Conclusion & Impact:The key findings in consequence of the experimental result are discussed in order to shed light on Android malware detection.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Android malware detection · mobile malware · mobile security

Abstract

Android, the most widely-used mobile operating system, attracts the attention of malware developers as well as benign users. Despite the serious proactive actions taken by Android, the Android malware is still widespread as a result of the increasing sophistication and the diversity of malware. Android malware detection systems are generally classified into two: (1) Static analysis, and (2) dynamic analysis. In this study, a novel Android malware detection framework, namely, Hybroid, was proposed which combines both the static and dynamic analysis techniques to benefit from the advantages of both of these techniques. An up-to-date version of Android, namely, Android Oreo, was specifically employed in order to handle the problem from an up-to-date perspective as the recent versions of Android provide new security mechanisms, which are discussed with this study. Hybroid was evaluated on a large dataset that consists of 10,658 applications, and the accuracy of Hybroid was calculated as high as 99.5% when it was utilized with the J48 classification algorithm which outperforms the state-of-the-art studies. The key findings in consequence of the experimental result are discussed in order to shed light on Android malware detection.

🔖

Cite This Article

Kabakuş, A. T. (2021). Hybroid: A Novel Hybrid Android Malware Detection Framework. Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi, Cilt: 14 Sayı: 1, 331–356. https://doi.org/10.18185/erzifbed.806683

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