Ribeiro, José, Mantas, Georgios ORCID: https://orcid.org/0000-0002-8074-0417, Saghezchi, Firooz B., Rodriguez, Jonathan, Shepherd, Simon J. and Abd-Alhameed, Raed A. (2018) Towards an autonomous host-based intrusion detection system for android mobile devices. In: Broadband Communications, Networks, and Systems. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (LNICST) (263). Springer, Cham, Switzerland, pp. 139-148. ISBN 978-3030-051945 ISSN 1867-8211 (doi:10.1007/978-3-030-05195-2_14)

Preview

PDF (Author Accepted Manuscript) 27703 MANTAS_Towards_an Autonomous_Host-Based_Intrusion_Detection_System_2018.pdf - Accepted Version Download (465kB) | Preview

Abstract

In the 5G era, mobile devices are expected to play a pivotal role in our daily life. They will provide a wide range of appealing features to enable users to access a rich set of high quality personalized services. However, at the same time, mobile devices (e.g., smartphones) will be one of the most attractive targets for future attackers in the upcoming 5G communications systems. Therefore, security mechanisms such as mobile Intrusion Detection Systems (IDSs) are essential to protect mobile devices from a plethora of known and unknown security breaches and to ensure user privacy. However, despite the fact that a lot of research effort has been placed on IDSs for mobile devices during the last decade, autonomous host-based IDS solutions for 5G mobile devices are still required to protect them in a more efficient and effective manner. Towards this direction, we propose an autonomous host-based IDS for Android mobile devices applying Machine Learning (ML) methods to inspect different features representing how the device’s resources (e.g., CPU, memory, etc.) are being used. The simulation results demonstrate a promising detection accuracy of above 85%, reaching up to 99.99%.

Item Type:	Conference Proceedings
Title of Proceedings:	Broadband Communications, Networks, and Systems
Additional Information:	Proceedings of the 9th International EAI Conference, Broadnets 2018, Faro, Portugal, September 19–20, 2018.
Uncontrolled Keywords:	Mobile Intrusion Detection System, Android, Security, 5G Communications, Machine Learning, Malware Detection, Host-based IDS
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Engineering (ENG)
Last Modified:	02 Nov 2020 13:40
URI:	http://gala.gre.ac.uk/id/eprint/27703

Actions (login required)

View Item

Downloads