DEEPSEL: A novel feature selection for early identification of malware in mobile applications

Journal article


Muhammad Ajmal Azad, Farhan Riaz, Anum Aftab, Syed Khurram Jah Rizvi, Junaid Arshad, Hany F. Atlam and Atlam, H. 2021. DEEPSEL: A novel feature selection for early identification of malware in mobile applications. Future Generation Computer Systems. 129, pp. 54-63. https://doi.org/10.1016/j.future.2021.10.029
AuthorsMuhammad Ajmal Azad, Farhan Riaz, Anum Aftab, Syed Khurram Jah Rizvi, Junaid Arshad, Hany F. Atlam and Atlam, H.
Abstract

Smartphone applications have gained popularity in recent years due to the large footprint of mobile phone usage and availability of a large number of value-added applications. The official app stores (google, IOS, Microsoft, Amazon) provide a platform for hosting, publishing, distributing, and managing the mobile applications developed by companies and individuals. This mobile application ecosystem could be used to distribute the malicious apps which are specifically designed to track behavior of users, spy on the activities of users, and could be a threat to the privacy, confidentiality, and integrity of the users. In this paper, we present a novel approach called DEEPSEL (Deep Feature Selection), a deep learning-based method for the identification of malware and malicious codes within android applications. DEEPSEL uses a set of features to characterize the behavior of android applications and classify them as legitimate and malicious. The main contribution is characterized by the usage of particle swarm optimization for performing feature selection. We evaluated our approach on a public malware data-set which is composed of samples collected from 39 unique malware families. Our results show that the proposed method can achieve very good results with an accuracy of around 83.6% and an F-measure of around 82.5%.

KeywordsFeature selection; Malware detection; Mobile applications; Deep learning
Year2021
JournalFuture Generation Computer Systems
Journal citation129, pp. 54-63
PublisherElsevier
ISSN1872-7115
Digital Object Identifier (DOI)https://doi.org/10.1016/j.future.2021.10.029
Web address (URL)https://doi.org/10.1016/j.future.2021.10.029
Output statusPublished
Publication dates16 Nov 2021
Publication process dates
Accepted26 Oct 2022
Deposited24 May 2023
Permalink -

https://repository.derby.ac.uk/item/9yy41/deepsel-a-novel-feature-selection-for-early-identification-of-malware-in-mobile-applications

  • 18
    total views
  • 0
    total downloads
  • 0
    views this month
  • 0
    downloads this month

Export as

Related outputs

Deep labeller: automatic bounding box generation for synthetic violence detection datasets
Nadeem, M., Kurugollu, F., Saravi, S., Atlam, H. and Franqueira, V. 2023. Deep labeller: automatic bounding box generation for synthetic violence detection datasets. Multimedia Tools and Applications. pp. 1-18. https://doi.org/10.1007/s11042-023-15621-5
Business Email Compromise Phishing Detection Based on Machine Learning: A Systematic Literature Review
Atlam, H. and Olayonu Oluwatimilehin 2022. Business Email Compromise Phishing Detection Based on Machine Learning: A Systematic Literature Review. Electronics. 12 (1), pp. 1-28. https://doi.org/10.3390/electronics12010042
ANFIS for risk estimation in risk-based access control model for smart homes
Atlam, H. and Gary B. Wills 2022. ANFIS for risk estimation in risk-based access control model for smart homes. Multimedia Tools and Applications. pp. 1-30. https://doi.org/10.1007/s11042-022-14010-8
IoT forensics: A state-of-the-art review, callenges and future directions
Alenezi, Ahmed, Atlam, Hany, Alsagri, Reem, Alassafi, Madini and Wills, Gary 2019. IoT forensics: A state-of-the-art review, callenges and future directions. SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0007905401060115
Experts reviews of a cloud forensic readiness framework for organizations
Alenezi, Ahmed, Atlam, Hany F. and Wills, Gary B. 2019. Experts reviews of a cloud forensic readiness framework for organizations. Journal of Cloud Computing. 8 (1). https://doi.org/10.1186/s13677-019-0133-z
Security, cybercrime and digital forensics for IoT
Atlam, Hany F., Alenezi, Ahmed, Alassafi, Madini O., Alshdadi, Abdulrahman A. and Wills, Gary B. 2019. Security, cybercrime and digital forensics for IoT. in: Intelligent Systems Reference Library Springer International Publishing.
A famework for data sharing between healthcare providers using blockchain
Alzahrani, Ahmed G., Alenezi, Ahmed, Atlam, Hany F. and Wills, Gary 2020. A famework for data sharing between healthcare providers using blockchain. Proceedings of the 5th International Conference on Internet of Things, Big Data and Security. https://doi.org/10.5220/0009413403490358
Intersections between IoT and distributed ledger
Atlam, Hany F. and Wills, Gary B. 2019. Intersections between IoT and distributed ledger. in: Advances in Computers Elsevier.
IoT security, privacy, safety and ethics
Atlam, Hany F. and Wills, Gary B. 2019. IoT security, privacy, safety and ethics. in: Internet of Things Springer International Publishing.
Fuzzy logic with expert judgment to implement an adaptive risk-based access control model for IoT
Atlam, Hany F., Walters, Robert J., Wills, Gary B. and Daniel, Joshua 2019. Fuzzy logic with expert judgment to implement an adaptive risk-based access control model for IoT. Mobile Networks and Applications. https://doi.org/10.1007/s11036-019-01214-w
A validation of security determinants model for cloud adoption in Saudi organisations’ context
Alassafi, Madini O., Atlam, Hany F., Alshdadi, Abdulrahman A., Alzahrani, Abdullah I., AlGhamdi, Rayed A. and Buhari, Seyed M. 2019. A validation of security determinants model for cloud adoption in Saudi organisations’ context. International Journal of Information Technology. https://doi.org/10.1007/s41870-019-00360-4
An efficient security risk estimation technique for Risk-based access control model for IoT
Atlam, Hany F. and Wills, Gary 2019. An efficient security risk estimation technique for Risk-based access control model for IoT. Internet of Things. https://doi.org/10.1016/j.iot.2019.100052