Version 1
: Received: 6 June 2019 / Approved: 7 June 2019 / Online: 7 June 2019 (13:45:28 CEST)
How to cite:
Qamar, A.; Karim, A.; Shamshirband, S. Hybrid Machine Learning Techniques Applied in Real Engineering Applications. Preprints2019, 2019060060. https://doi.org/10.20944/preprints201906.0060.v1
Qamar, A.; Karim, A.; Shamshirband, S. Hybrid Machine Learning Techniques Applied in Real Engineering Applications. Preprints 2019, 2019060060. https://doi.org/10.20944/preprints201906.0060.v1
Qamar, A.; Karim, A.; Shamshirband, S. Hybrid Machine Learning Techniques Applied in Real Engineering Applications. Preprints2019, 2019060060. https://doi.org/10.20944/preprints201906.0060.v1
APA Style
Qamar, A., Karim, A., & Shamshirband, S. (2019). Hybrid Machine Learning Techniques Applied in Real Engineering Applications. Preprints. https://doi.org/10.20944/preprints201906.0060.v1
Chicago/Turabian Style
Qamar, A., Ahmad Karim and Shahab Shamshirband. 2019 "Hybrid Machine Learning Techniques Applied in Real Engineering Applications" Preprints. https://doi.org/10.20944/preprints201906.0060.v1
Abstract
Smartphone devices, particularly android devices used by billions of people everywhere in the world. Similarly, this increasing rate attracts mobile botnet attackers that is a network of interconnected nodes operated by command and control (C&C) method to expand malicious activities. At present, mobile botnet attacks carried Distributed denial of services (DDoS) that causes to steal sensitive data, remote access, spam generation, etc. Consequently, various approaches are defined in the literature to detect mobile botnet using static or dynamic analysis. In this paper, we have proposed a novel hybrid model, which is a combination of static and dynamic method that relies on machine learning to identify android botnet applications having C&C capability. The results evaluated through machine learning classifiers in which Random forest classifier outperform other ML techniques, i.e. Naïve Bayes, Support Vector Machine, and Simple logistics. Our proposed framework can achieve 97.48% accuracy in detecting such harmful applications. Furthermore, we highlight some research directions and possible solutions regarding botnet attacks for the entire community.
Computer Science and Mathematics, Applied Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
