Android malware detection using hybrid ANFIS architecture with low computational cost convolutional layers

ATACAK İ., Kılıç K., DOĞRU İ. A.

PeerJ Computer Science, vol.8, 2022 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 8
  • Publication Date: 2022
  • Doi Number: 10.7717/peerj-cs.1092
  • Journal Name: PeerJ Computer Science
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, Directory of Open Access Journals
  • Keywords: Malware detection, Mobile security, Convolutional neural network, Fuzzy logic, Permission
  • Yozgat Bozok University Affiliated: Yes


© Copyright 2022 Atacak et al.Background. Android is the most widely used operating system all over the world. Due to its open nature, the Android operating system has become the target of malicious coders. Ensuring privacy and security is of great importance to Android users. Methods. In this study, a hybrid architecture is proposed for the detection of Android malware from the permission information of applications. The proposed architecture combines the feature extraction power of the convolutional neural network (CNN) architecture and the decision making capability of fuzzy logic. Our method extracts features from permission information with a small number of filters and convolutional layers, and also makes the feature size suitable for ANFIS input. In addition, it allows the permission information to affect the classification without being neglected. In the study, malware was obtained from two different sources and two different data sets were created. In the first dataset, Drebin was used for malware applications, and in the second dataset, CICMalDroid 2020 dataset was used for malware applications. For benign applications, the Google Play Store environment was used. Results. With the proposed method, 92% accuracy in the first data set and 92% F-score value in the weighted average was achieved. In the second data set, an accuracy of 94.6% and an F-score of 94.6% on the weighted average were achieved. The results obtained in the study show that the proposed method outperforms both classical machine learning algorithms and fuzzy logic-based studies