We propose USBeat, a project that focuses on the vulnerabilities of USB devices and centers on the development of a comprehensive detection framework that relies upon a crucial attack repository. The framework of USBeat is aimed at accurate detection of both known and unknown USB-based attacks by a process that efficiently enhances the framework's detection capabilities over time. The framework integrates two main security approaches in order to enhance the detection of USB-based attacks associated with a variety of USB devices.
The first approach is aimed at the detection of known attacks and their variants, whereas the second approach focuses on the detection of unknown attacks. USBeat will consist of six independent but complimentary detection modules, each detecting attacks based on a different approach or discipline. These modules include novel ideas and algorithms inspired from or already developed within our team's domains of expertise, including cyber security, electrical and signal processing, machine learning, and computational biology.
The establishment and maintenance of the USBeat dynamic and up-to-date attack repository will strengths the capabilities of the USBeat detection framework. The attack repository’s infrastructure enables researchers to record, document, create, and simulate existing and new USB-based attacks. This data is used to maintain the detection framework’s updatability by incorporating knowledge regarding new attacks.