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

In this research, we utilized the energy consumption pattern as a feature to train a classifier to detect potentially compromised Internet of Things (IoT) devices.

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.

For more information please refer to: https://cybersciencelab.org/detecting-crypto-ransomware-in-iot-networks-based-on-energy-consumption-footprint/