Deceiving supervised machine learning models via adversarial data poisoning attacks: a case study with USB keyboards

Abstract

Due to its plug-and-play functionality and wide device support, the universal serial bus (USB) protocol has become one of the most widely used protocols. However, this widespread adoption has introduced a significant security concern: the implicit trust provided to USB devices, which has created a vast array of attack vectors. Malicious USB devices exploit this trust by disguising themselves as benign peripherals and covertly implanting malicious commands into connected host devices. Existing research employs supervised learning models to identify such malicious devices, but our study reveals a weakness in these models when faced with sophisticated data poisoning attacks. We propose, design and implement a sophisticated adversarial data poisoning attack to demonstrate how these models can be manipulated to misclassify an attack device as a benign device. Our method entails generating keystroke data using a microprogrammable keystroke attack device. We develop adversarial attacker by meticulously analyzing the data distribution of data features generated via USB keyboards from benign users. The initial training data is modified by exploiting firmware-level modifications within the attack device. Upon evaluating the models, our findings reveal a significant decrease from 99 to 53% in detection accuracy when an adversarial attacker is employed. This work highlights the critical need to reevaluate the dependability of machine learning-based USB threat detection mechanisms in the face of increasingly sophisticated attack methods. The vulnerabilities demonstrated highlight the importance of developing more robust and resilient detection strategies to protect against the evolution of malicious USB devices.