Towards Automated Classification of Firmware Images and Identification of Embedded Devices
Costin, A., Zarras, A., & Francillon, A. (2017). Towards Automated Classification of Firmware Images and Identification of Embedded Devices. In S. De Capitani di Vimercati, & F. Martinelli (Eds.), ICT Systems Security and Privacy Protection : 32nd IFIP TC 11 International Conference, SEC 2017, Rome, Italy, May 29-31, 2017, Proceedings (pp. 233-247). IFIP Advances in Information and Communication Technology, 502. Cham: Springer. doi:10.1007/978-3-319-58469-0_16
Julkaistu sarjassaIFIP Advances in Information and Communication Technology;502
© IFIP International Federation for Information Processing 2017. This is a final draft version of an article whose final and definitive form has been published by Springer. Published in this repository with the kind permission of the publisher.
Embedded systems, as opposed to traditional computers, bring an incredible diversity. The number of devices manufactured is constantly increasing and each has a dedicated software, commonly known as firmware. Full firmware images are often delivered as multiple releases, correcting bugs and vulnerabilities, or adding new features. Unfortunately, there is no centralized or standardized firmware distribution mechanism. It is therefore difficult to track which vendor or device a firmware package belongs to, or to identify which firmware version is used in deployed embedded devices. At the same time, discovering devices that run vulnerable firmware packages on public and private networks is crucial to the security of those networks. In this paper, we address these problems with two different, yet complementary approaches: firmware classification and embedded web interface fingerprinting. We use supervised Machine Learning on a database subset of real world firmware files. For this, we first tell apart firmware images from other kind of files and then we classify firmware images per vendor or device type. Next, we fingerprint embedded web interfaces of both physical and emulated devices. This allows recognition of web-enabled devices connected to the network. In some cases, this complementary approach allows to logically link web-enabled online devices with the corresponding firmware package that is running on the devices. Finally, we test the firmware classification approach on 215 images with an accuracy of 93.5%, and the device fingerprinting approach on 31 web interfaces with 89.4% accuracy. ...