Detecting crypto-ransomware in IoT networks based on energy consumption footprint

Azmoodeh, A, Dehghantanha, A ORCID: 0000-0002-9294-7554, Conti, M and Raymond Choo, K-K 2017, 'Detecting crypto-ransomware in IoT networks based on energy consumption footprint' , Journal of Ambient Intelligence and Humanized Computing .

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Abstract

An Internet of Things (IoT) architecture generally consists of a wide range of Internet-connected devices or things such as Android devices, and devices that have more computational capabilities (e.g., storage capacities) are likely to be targeted by ransomware authors. In this paper, we present a machine learning based approach to detect ransomware attacks by monitoring power consumption of Android devices. Specifically, our proposed method monitors the energy consumption patterns of different processes to classify ransomware from non-malicious applications. We then demonstrate that our proposed approach out-performs K-Nearest Neighbors, Neural Networks, Support Vector Machine and Random Forest, in terms of accuracy rate, recall rate, precision rate and F-measure.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre (SIRC)
Journal or Publication Title: Journal of Ambient Intelligence and Humanized Computing
Publisher: Springer
ISSN: 1868-5137
Related URLs:
Funders: European Council International Incoming Fellowship
Depositing User: Dr. Ali Dehghantanha
Date Deposited: 01 Aug 2017 13:29
Last Modified: 12 Dec 2017 07:07
URI: http://usir.salford.ac.uk/id/eprint/43410

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