An Effective Android Ransomware Detection Through Multi-Factor Feature Filtration and Recurrent Neural Network

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Bibi, Iram, Akhunzada, Adnan, Malik, Jahanzaib, Ahmed, Ghufran and Raza, Mohsin (2019) An Effective Android Ransomware Detection Through Multi-Factor Feature Filtration and Recurrent Neural Network. In: 2019 UK/China Emerging Technologies, UCET 2019. 2019 UK/China Emerging Technologies, UCET 2019 . The Institute of Electrical and Electronics Engineers (IEEE), GBR. ISBN 9781728127972

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Abstract

with the increasing diversity of Android malware, the effectiveness of conventional defense mechanisms are at risk. This situation has endorsed a notable interest in the improvement of the exactitude and scalability of malware detection for smart devices. In this study, we have proposed an effective deep learning-based malware detection model for competent and improved ransomware detection in Android environment by looking at the algorithm of Long Short-Term Memory (LSTM). The feature selection has been done using 8 different feature selection algorithms. The 19 important features are selected through simple majority voting process by comparing results of all feature filtration techniques. The proposed algorithm is evaluated using android malware dataset (CI-CAndMal2017) and standard performance parameters. The proposed model outperforms with 97.08% detection accuracy. Based on outstanding performance, we endorse our proposed algorithm to be efficient in malware and forensic analysis.

Item Type:	Book Section
Additional Information:	Publisher Copyright: © 2019 IEEE.
Uncontrolled Keywords:	android malware,deep learning,long short-term memory,ransomware,recurrent neural network,security,computer networks and communications,computer vision and pattern recognition,information systems and management,energy engineering and power technology,health informatics,instrumentation ,/dk/atira/pure/subjectarea/asjc/1700/1705
Faculty \ School:	Faculty of Science > School of Computing Sciences
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	16 Jun 2025 10:31
Last Modified:	17 Jun 2025 06:30
URI:	https://ueaeprints.uea.ac.uk/id/eprint/99550
DOI:	10.1109/UCET.2019.8881884

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