Detection and analysis of android malwares using hybrid dual Path bi-LSTM Kepler dynamic graph convolutional network
- Title
- Detection and analysis of android malwares using hybrid dual Path bi-LSTM Kepler dynamic graph convolutional network
- Creator
- Lingayya S.; Kulkarni P.; Salins R.D.; Uppoor S.; Gurudas V.R.
- Description
- In past decade, the android malware threats have been rapidly increasing with the widespread usage of internet applications. In respect of security purpose, there are several machine learning techniques attempted to detect the malwares effectively, but failed to achieve the accurate detection due to increasing number of features, more time consumption decreases in detection efficiency. To overcome these limitations, in this research work an innovative Hybrid dual path Bidirectional long short-term memory Kepler dynamic graph Convolutional Network (HBKCN) is proposed to analyze and detect android malwares effectively. First, the augmented abstract syntax tree is applied for pre-processing and extracts the string function from each malware. Second, the adaptive aphid ant optimization is utilized to choose the most appropriate features and remove irrelevant features. Finally, the proposed HBKCN classifies benign and malware apps based on their specifications. Four benchmark datasets, namely Drebin, VirusShare, Malgenome -215, and MaMaDroid datasets, are employed to estimate the effectiveness of the technique. The result demonstrates that the HBKCN technique achieved excellent performance with respect to a few important metrics compared to existing methods. Moreover, detection accuracies of 99.2%, 99.1%,99.8% and 99.8% are achieved for the considered datasets, respectively. Also, the computation time is greatly reduced, illustrating the efficiency of the proposed model in identifying android malwares. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
- Source
- International Journal of Machine Learning and Cybernetics
- Date
- 2024-01-01
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- Android application; Benign; Dynamic graph; Malicious; Selected features; Syntax tree
- Coverage
- Lingayya S., Department of Computer Science and Engineering, Shri Madhwa Vadiraja Institute of Technology and Management, Karnataka, Bantakal, 574115, India; Kulkarni P., Department of Computer Science and Engineering, School of Engineering, Dayananda Sagar University, Ramanagara Dt., Bengaluru,, 562112, India; Salins R.D., Department of Aerospace, Honeywell Technology Solutions Lab Pvt. Ltd, Bellandur, Bangalore, 560103, India; Uppoor S., Infosys Ltd, Karnataka, Mangalore, 574153, India; Gurudas V.R., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST University, Karnataka, Bangalore, India
- Rights
- Restricted Access
- Relation
- ISSN: 18688071
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Lingayya S.; Kulkarni P.; Salins R.D.; Uppoor S.; Gurudas V.R., “Detection and analysis of android malwares using hybrid dual Path bi-LSTM Kepler dynamic graph convolutional network,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/13533.