Poster: Adversarial Perturbation Attacks on the State-of-the-Art Cryptojacking Detection System in IoT Networks

Abstract

The popularity of cryptocurrency raised a new cyber security threat dubbed cryptojacking representing malicious activities for abusing victims' computing resources without their consent to mine cryptocurrency. Recently, Tekiner et al. [1] proposed an effective cryptojacking detection technique using a machine learning model with the statistical properties of the network traffic for cryptojacking in the Internet of Things (IoT) devices. In this paper, however, we demonstrate that this state-of-the-art method can effectively be evaded by maliciously manipulating the network packets for cryptojacking. Our evaluation results show that packet manipulations (packet splitting, dummy packet/payload insertion, and a proxy network) can effectively evade the model's detection-the packet splitting technique significantly decreased the F1-score of the detection model from 0.93 to 0.30. Finally, the best combination of those packet manipulations can decrease the F1-score of the detection model to 0.21.