@Article{cmc.2022.028680,
AUTHOR = {Mamona Arshad, Ahmad Karim, Salman Naseer, Shafiq Ahmad, Mejdal Alqahtani, Akber Abid Gardezi, Muhammad Shafiq, Jin-Ghoo Choi},
TITLE = {Detecting Android Botnet Applications Using Convolution Neural Network},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {77},
YEAR = {2023},
NUMBER = {2},
PAGES = {2123--2135},
URL = {http://www.techscience.com/cmc/v77n2/54784},
ISSN = {1546-2226},
ABSTRACT = {The exponential growth in the development of smartphones and handheld devices is permeated due to everyday
activities i.e., games applications, entertainment, online banking, social network sites, etc., and also allow the end
users to perform a variety of activities. Because of activities, mobile devices attract cybercriminals to initiate an
attack over a diverse range of malicious activities such as theft of unauthorized information, phishing, spamming,
Distributed Denial of Services (DDoS), and malware dissemination. Botnet applications are a type of harmful
attack that can be used to launch malicious activities and has become a significant threat in the research area.
A botnet is a collection of infected devices that are managed by a botmaster and communicate with each other
via a command server in order to carry out malicious attacks. With the rise in malicious attacks, detecting botnet
applications has become more challenging. Therefore, it is essential to investigate mobile botnet attacks to uncover
the security issues in severe financial and ethical damages caused by a massive coordinated command server.
Current state of the art, various solutions were provided for the detection of botnet applications, but in general,
the researchers suffer various techniques of machine learning-based methods with static features which are usually
ineffective when obfuscation techniques are used for the detection of botnet applications. In this paper, we propose
an approach by exploring the concept of a deep learning-based method and present a well-defined Convolutional
Neural Network (CNN) model. Using the visualization approach, we obtain the colored images through byte code
files of applications and perform an experiment. For analysis of the results of an experiment, we differentiate the
performance of the model from other existing research studies. Furthermore, our method outperforms with 94.34%
accuracy, 92.9% of precision, and 92% of recall.},
DOI = {10.32604/cmc.2022.028680}
}