A Survey on Image-Based Approaches for Android Malware Detection: Toward Sustainable and Efficient Solutions
Book of Abstracts of the International Conference on Sustainability – Transforming Higher Education for a Sustainable Future, Susana Leal,Cláudio Barradas,Ana Loureiro,Inês Messias,Sandra Oliveira, Editör, IPSantarém, Lisbon, ss.109, 2025
- Yayın Türü: Kitapta Bölüm / Araştırma Kitabı
- Basım Tarihi: 2025
- Yayınevi: IPSantarém
- Basıldığı Şehir: Lisbon
- Sayfa Sayıları: ss.109
- Editörler: Susana Leal,Cláudio Barradas,Ana Loureiro,Inês Messias,Sandra Oliveira, Editör
- Gazi Üniversitesi Adresli: Evet
Özet
The rapid proliferation of mobile device usage has introduced significant cybersecurity
threats. Among them, the Android operating system has become one of the primary targets
for malware developers due to its open-source nature and vast user base. While traditional
malware detection methods rely on static and dynamic analysis techniques, these often
require extensive preprocessing and expert-driven feature extraction and selection, which
can limit their efficiency and introduce performance bottlenecks. Furthermore,
conventional approaches may fall short when confronted with advanced and sophisticated
malware variants. In recent years, deep learning and image processing-based approaches
have emerged as innovative and effective alternatives for malware detection. These
methods not only enhance detection accuracy but also contribute to a sustainable and
scalable cybersecurity infrastructure through their automation capabilities. This survey
reviews the current literature on image-based methods for Android malware detection,
providing a detailed analysis of the applied techniques, their strengths, and their
limitations. In particular, approaches involving deep learning, convolutional neural
networks (CNNs), and other machine learning algorithms are comparatively evaluated. The
findings indicate that image-based analysis methods offer more reliable, comprehensive,
and effective detection than traditional techniques. Moreover, these approaches hold
significant promise for advancing sustainable digital security systems. Recommendations
for future research directions are also presented, along with a discussion of the potential
contributions to the academic body of knowledge in this domain.