In the era of edge computing and Artificial Intelligence (AI), securing billions of edge devices within a network against intelligent attacks is crucial. We propose PUFGAN, an innovative machine learning attack-proof security architecture, by embedding a self-adversarial agent within a device fingerprint- based security primitive, public PUF (PPUF) known for its strong fingerprint-driven cryptography. The self-adversarial agent is implemented using Generative Adversarial Networks (GANs). The agent attempts to self-attack the system based on two GAN variants, vanilla GAN and conditional GAN. By turning the attacking quality through generating realistic secret keys used in the PPUF primitive into system vulnerability, the security architecture is able to monitor its internal vulnerability. If the vulnerability level reaches at a specific value, PUFGAN allows the system to restructure its underlying security primitive via feedback to the PPUF hardware, maintaining security entropy at as high a level as possible.We evaluated PUFGAN on three different machine environments: Google Colab, a desktop PC, and a Raspberry Pi 2, using a real-world PPUF dataset. Extensive experiments demonstrated that even a strong device fingerprint security primitive can become vulnerable, necessitating active restructuring of the current primitive, making the system resilient against extreme attacking environments.
|Title of host publication||INFOCOM 2020 - IEEE Conference on Computer Communications|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||10|
|State||Published - Jul 2020|
|Event||38th IEEE Conference on Computer Communications, INFOCOM 2020 - Toronto, Canada|
Duration: 6 Jul 2020 → 9 Jul 2020
|Name||Proceedings - IEEE INFOCOM|
|Conference||38th IEEE Conference on Computer Communications, INFOCOM 2020|
|Period||6/07/20 → 9/07/20|
Bibliographical noteFunding Information:
This work was supported by Samsung Research Funding & Incubation Center of Samsung Electronics under Project Number SRFC-IT1803-00.
© 2020 IEEE.