Bot-IMG: A framework for image-based detection of Android botnets using machine learning

Date

2021-11-30

Advisors

Journal Title

Journal ISSN

ISSN

Volume Title

Publisher

IEEE

Type

Conference

Peer reviewed

Yes

Abstract

To enable more effective mitigation of Android botnets, image-based detection approaches offer great promise. Such image-based or visualization methods provide detection solutions that are less reliant on hand-engineered features which require domain knowledge. In this paper we propose Bot- IMG, a framework for visualization and image-based detection of Android botnets using machine learning. Furthermore, we evaluated the efficacy of Bot-IMG framework using the ISCX botnet dataset. In particular, we implement an image- based detection method using Histogram of Oriented Gradients (HOG) as feature descriptors within the framework, and utilized Autoencoders in conjunction with traditional machine learning classifiers. From the experiments performed, we obtained up to 95.3% classification accuracy using train-test split of 80:20 and 93.1% classification accuracy with 10-fold cross validation.

Description

The file attached to this record is the author's final peer reviewed version.

Keywords

Botnet detection, Image processing, Histogram of Oriented Gradients, Machine learning, Autoencoder, Android Malware

Citation

Yerima, S. Y. and Bashar, A. (2021) Bot-IMG: A framework for image-based detection of Android botnets using machine learning. 18th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA 2021), Tangier, Morocco, 30 Nov – 3 Dec 2021.

Rights

Research Institute

Cyber Technology Institute (CTI)