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

详细信息

发表期刊/会议

Journal of Ambient Intelligence and Humanized Computing

发表日期

2017-08-23

发表年份

2017

摘要

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 outperforms K-Nearest Neighbors, Neural Networks, Support Vector Machine and Random Forest, in terms of accuracy rate, recall rate, precision rate and F-measure.