loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Jelena Milosevic 1 ; Miroslaw Malek 1 and Alberto Ferrante 2

Affiliations: 1 Universitá della Svizzera italiana, Switzerland ; 2 Universitá della Svizzera Italiana, Switzerland

Keyword(s): Malware Detection, Dynamic Detection, Android, Internet of Things (IoT).

Related Ontology Subjects/Areas/Topics: Information and Systems Security ; Security and Privacy in Mobile Systems ; Software Security

Abstract: With an ever-increasing and ever more aggressive proliferation of malware, its detection is of utmost importance. However, due to the fact that IoT devices are resource-constrained, it is difficult to provide effective solutions. The main goal of this paper is the development of lightweight techniques for dynamic malware detection. For this purpose, we identify an optimized set of features to be monitored at runtime on mobile devices as well as detection algorithms that are suitable for battery-operated environments. We propose to use a minimal set of most indicative memory and CPU features reflecting malicious behavior. The performance analysis and validation of features usefulness in detecting malware have been carried out by considering the Android operating system. The results show that memory and CPU related features contain enough information to discriminate between execution traces belonging to malicious and benign applications with significant detection precision and recall. Since the proposed approach requires only a limited number of features and algorithms of low complexity, we believe that it can be used for effective malware detection, not only on mobile devices, but also on other smart elements of IoT. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.238.62.119

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Milosevic, J.; Malek, M. and Ferrante, A. (2016). A Friend or a Foe? Detecting Malware using Memory and CPU Features. In Proceedings of the 13th International Joint Conference on e-Business and Telecommunications (ICETE 2016) - SECRYPT; ISBN 978-989-758-196-0; ISSN 2184-3236, SciTePress, pages 73-84. DOI: 10.5220/0005964200730084

@conference{secrypt16,
author={Jelena Milosevic. and Miroslaw Malek. and Alberto Ferrante.},
title={A Friend or a Foe? Detecting Malware using Memory and CPU Features},
booktitle={Proceedings of the 13th International Joint Conference on e-Business and Telecommunications (ICETE 2016) - SECRYPT},
year={2016},
pages={73-84},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005964200730084},
isbn={978-989-758-196-0},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on e-Business and Telecommunications (ICETE 2016) - SECRYPT
TI - A Friend or a Foe? Detecting Malware using Memory and CPU Features
SN - 978-989-758-196-0
IS - 2184-3236
AU - Milosevic, J.
AU - Malek, M.
AU - Ferrante, A.
PY - 2016
SP - 73
EP - 84
DO - 10.5220/0005964200730084
PB - SciTePress