Enhanced Federated Learning Framework based on Deep Learning and Neutrosophic Set for Android Malware Classification

Authors

  • Mohamed Refaat Abdellah The Department of Computer Science, College of Information Technology, Misr University for Science and Technology, Cairo, Egypt
  • Hasan H. Oudah College of Arts / Department of Journalism, Ahl Al Bayt University, Karbala, Iraq.
  • Ahmed Mohamed Ahmed Badawy Cybersecurity Department, Faculty of Computers and Artificial Intelligence, Helwan University, Egypt.
  • Mohamed AbdElFattah AbdElFattah M Hassan Cybersecurity Department, Faculty of Computers and Artificial Intelligence, Helwan University, Egypt.
  • Shady Ahmed Bedier Computer Science Department, Faculty of Information System and Computer Science, October 6 University, Giza, 12585, Egypt;
  • Ahmed A. Metwaly Department of Computer Science, Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Egypt.
  • Mohamed eassa Computer Science Department, Faculty of Information System and Computer Science, October 6 University, Giza, 12585, Egypt; Applied Science Research Center. Applied Science Private University, Amman, Jordan
  • Ahmed Abdelhafeez Computer Science Department, Faculty of Information System and Computer Science, October 6 University, Giza, 12585, Egypt; Applied Science Research Center. Applied Science Private University, Amman, Jordan

Keywords:

Malware detection; Deep Learning; Federated Learning; Interval Valued Neutrosophic Sets

Abstract

 Malware detection is one of the critical tasks of cybersecurity, especially considering the growing 
popularity of mobile devices. The integrity and security of mobile ecosystems rely on the capacity to 
identify malware quickly and precisely, as security hacks against these devices become increasingly 
frequent. Due to the requirement for massive volumes of data aggregation, traditional centralized machine 
learning (ML) techniques for malware detection face challenges with data sharing, computational 
complexity, and privacy. This research addresses these challenges by proposing a novel model, called 
"CNN-Fed", which is based on Convolutional Neural Networks (CNN) with Federated Learning (Fed). The 
main goal of this work is to create a global classifier for Android malware detection that is highly accurate 
and does not require centralised data aggregation. Using four benchmark datasets—Drebin, Malgenome, 
Kronodroid, and Tuandromd—the CNN-Fed model is trained across many clients in a federated setting 
under the suggested architecture. Then we use multi-criteria decision-making (MCDM) methodology to 
evaluate the number of clients and number of rounds. We use two MCDM methods such as Entropy 
methodology to compute the criteria weights and the CoCoSo methodology to rank the alternatives. We 
use the interval-valued neutrosophic sets (IVNSs) to deal with uncertainty and vague information. We use 
five criteria such as accuracy, F1-score, precision, recall, and FP and six of alternatives refer to the number 
of clients and number of rounds. The results show the best alternative has a larger number of clients and 
rounds. 

 

DOI: 10.5281/zenodo.15069485

Downloads

Download data is not yet available.

Downloads

Published

2025-05-01

How to Cite

Mohamed Refaat Abdellah, Hasan H. Oudah, Ahmed Mohamed Ahmed Badawy, Mohamed AbdElFattah AbdElFattah M Hassan, Shady Ahmed Bedier, Ahmed A. Metwaly, Mohamed eassa, & Ahmed Abdelhafeez. (2025). Enhanced Federated Learning Framework based on Deep Learning and Neutrosophic Set for Android Malware Classification. Neutrosophic Sets and Systems, 82, 781-799. https://fs.unm.edu/nss8/index.php/111/article/view/6060

Most read articles by the same author(s)

1 2 > >>