AKANDO: A Hybrid Approach for Effective Android Botnet Detection

N., Akwukwuma Veronica, O., Egwali Annie, Doris, Asuquo

Innovative Multidisciplinary Journal of Applied Technology · 2024-yil

Annotatsiya

The ubiquitous nature of Android devices has regrettably rendered them prime targets for cyberattacks, particularly those orchestrated by botnets. Conventional solutions for Android botnet detection often rely on static or dynamic analysis techniques individually, leading to limitations in accuracy and adaptability. Existing research on Android malware detection utilizes various approaches, each with limitations. Some researchers focus on static analysis, examining code for signs of malicious activity. Others employ dynamic analysis, monitoring app behavior during runtime to detect suspicious system calls. Additionally, signature-based approaches compare apps to known malware signatures. However, research on anomaly-based detection, which identifies unusual app behavior without relying on pre-defined patterns, remains limited, leaving room for improvement. This paper proposes AKANDO, a novel botnet detection model that leverages a hybrid feature extraction approach and a multi-layered neural network architecture to achieve superior performance. By combining static and dynamic analysis, AKANDO gains a more holistic understanding of app behavior, potentially leading to superior detection accuracy compared to methods relying solely on one approach. The neural network architecture allows AKANDO to learn complex relationships between extracted features and botnet behavior, enabling it to adapt to evolving threats through continuous training with updated data. Moreover, AKANDO prioritizes minimizing false positives through its hybrid analysis and machine learning techniques, ensuring legitimate applications aren't flagged as malicious.

Maqola ma’lumotlari
MualliflarN., Akwukwuma Veronica, O., Egwali Annie, Doris, Asuquo
JurnalInnovative Multidisciplinary Journal of Applied Technology
NashriyotТДТУ Қўқон филиали
ISSN2995-486X, 2995-486x
Nashr sanasi2024-06-16
Jild2
Son5
Betlar123-135
TilIngliz
Ilmiy sohaTexnika
Indeksga qo‘shilgan2026-08-26

Maqolaning asl sahifasi

Kalit so‘zlar

Android botnet detection, Hybrid analysis, Machine learning, Neural networks, False positives.

Ilmiy soha

Texnika

Iqtibos keltirish

APAN., Akwukwuma Veronica, O., Egwali Annie, & Doris, Asuquo (2024). AKANDO: A Hybrid Approach for Effective Android Botnet Detection. Innovative Multidisciplinary Journal of Applied Technology, 2(5), 123-135. https://www.multijournals.org/index.php/innovative/article/view/1535
GOST (OAK)N., Akwukwuma Veronica, O., Egwali Annie, Doris, Asuquo AKANDO: A Hybrid Approach for Effective Android Botnet Detection // Innovative Multidisciplinary Journal of Applied Technology. – 2024. – T. 2, № 5. – P. 123-135.
MLAN., Akwukwuma Veronica, O., Egwali Annie, and Doris, Asuquo. “AKANDO: A Hybrid Approach for Effective Android Botnet Detection.” Innovative Multidisciplinary Journal of Applied Technology, 2(5), 123-135, 2024. https://www.multijournals.org/index.php/innovative/article/view/1535
ChicagoN., Akwukwuma Veronica, O., Egwali Annie, and Doris, Asuquo. “AKANDO: A Hybrid Approach for Effective Android Botnet Detection.” Innovative Multidisciplinary Journal of Applied Technology, 2(5), 123-135 (2024). https://www.multijournals.org/index.php/innovative/article/view/1535
HarvardN., Akwukwuma Veronica, O., Egwali Annie, and Doris, Asuquo (2024) ‘AKANDO: A Hybrid Approach for Effective Android Botnet Detection’, Innovative Multidisciplinary Journal of Applied Technology, 2(5), 123-135. https://www.multijournals.org/index.php/innovative/article/view/1535
BibTeX
@article{nakwukwumaveronica2024,
  title = {AKANDO: A Hybrid Approach for Effective Android Botnet Detection},
  author = {N., Akwukwuma Veronica and O., Egwali Annie and Doris, Asuquo},
  journal = {Innovative Multidisciplinary Journal of Applied Technology},
  year = {2024},
  volume = {2},
  number = {5},
  pages = {123-135},
  url = {https://www.multijournals.org/index.php/innovative/article/view/1535}
}

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