Applications of artificial intelligence to detect android botnets: A Survey
IEEE Access
; : 1-1, 2022.
Article
in English
| Scopus | ID: covidwho-1948719
ABSTRACT
With the growing popularity of Android smart devices, and especially with the recent advances brought on by the COVID-19 pandemic on digital adoption and transformation, the importance of protecting these devices has grown, as they carry very sensitive data. Malicious attacks are targeting Android since it is open source and has the highest adoption rate among mobile platforms. Botnet attacks are one of the most often forgotten types of attacks. In addition, there is a lack of review papers that can clarify the state of knowledge and indicate research gaps in detecting android botnets. Therefore, in this paper, we conduct a literature review to highlight the contributions of several studies in the domain of Android Botnet detection. This study attempts to provide a comprehensive overview of the deployed AI apps for future academics interested in performing Android Botnet Detection studies. We focused on the applications of artificial intelligence and its two prominent subdomains, machine learning (ML) and deep learning (DL) techniques. The study presents available Android Botnet datasets suitable for detection using ML and DL algorithms. Moreover, this study provides an overview of the methodologies and tools utilized in APK analysis. The paper also serves as a comprehensive taxonomy of Android Botnet detection methods and highlights a number of challenges encountered while analyzing Android Botnet detection techniques. The research gaps indicated an absence of hybrid analysis research in the area, as well as a lack of an up-to-date dataset and a time-series dataset. The findings of this paper show valuable prospective directions for future research and development opportunities. Author
Android Attacks; Android Botnet Detection; Android Botnets; Android Security; Artificial Intelligence (AI); Botnet; Codes; Deep Learning (DL); Feature extraction; Machine Learning (ML); Malware; Smart devices; Static analysis; Taxonomy; Android (operating system); Android malware; Digital devices; Hydrostatic pressure; Metadata; Mobile security; Network security; Sensitive data; Time series analysis; Android attack; Android botnet; Android securities; Artificial intelligence; Botnet detections; Botnets; Code; Deep learning; Features extraction; Machine learning; Machine-learning; Malwares; Taxonomies
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Observational study
Language:
English
Journal:
IEEE Access
Year:
2022
Document Type:
Article
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