Profiled side-channel attacks represent a practical threat to digital devices, thereby having the potential to disrupt the foundation of e-commerce, Internet-of-Things (IoT), and smart cities. In the profiled side-channel attack, adversary gains knowledge about the target device by getting access to a cloned device. Though these two devices are different in real-world scenarios, yet, unfortunately, a large part of research works simplifies the setting by using only a single device for both profiling and attacking. There, the portability issue is conveniently ignored to ease the experimental procedure. In parallel to the above developments, machine learning techniques are used in recent literature demonstrating excellent performance in profiled side-channel attacks. Again, unfortunately, the portability is neglected.

In this paper, we consider realistic side-channel scenarios and commonly used machine learning techniques to evaluate the influence of portability on the efficacy of an attack. Our experimental results show that portability plays an important role and should not be disregarded as it contributes to a significant overestimate of the attack efficiency, which can easily be an order of magnitude size. After establishing the importance of portability, we propose a new model called the Multiple Device Model (MDM) that formally incorporates the device to device variation during a profiled side-channel attack. We show through experimental studies, how machine learning and MDM significantly enhances the capacity for practical side-channel attacks.
More precisely, we demonstrate how MDM can improve the performance of an attack by an order of magnitude, completely negating the influence of portability.

View More Papers

Precisely Characterizing Security Impact in a Flood of Patches...

Qiushi Wu (University of Minnesota), Yang He (University of Minnesota), Stephen McCamant (University of Minnesota), Kangjie Lu (University of Minnesota)

Read More

Packet-Level Signatures for Smart Home Devices

Rahmadi Trimananda (University of California, Irvine), Janus Varmarken (University of California, Irvine), Athina Markopoulou (University of California, Irvine), Brian Demsky...

Read More

The Attack of the Clones Against Proof-of-Authority

Parinya Ekparinya (University of Sydney), Vincent Gramoli (University of Sydney and CSIRO-Data61), Guillaume Jourjon (CSIRO-Data61)

Read More

Genotype Extraction and False Relative Attacks: Security Risks to...

Peter Ney (University of Washington), Luis Ceze (University of Washington), Tadayoshi Kohno (University of Washington)

Read More