2 research outputs found
Application Inference using Machine Learning based Side Channel Analysis
The proliferation of ubiquitous computing requires energy-efficient as well
as secure operation of modern processors. Side channel attacks are becoming a
critical threat to security and privacy of devices embedded in modern computing
infrastructures. Unintended information leakage via physical signatures such as
power consumption, electromagnetic emission (EM) and execution time have
emerged as a key security consideration for SoCs. Also, information published
on purpose at user privilege level accessible through software interfaces
results in software only attacks. In this paper, we used a supervised learning
based approach for inferring applications executing on android platform based
on features extracted from EM side-channel emissions and software exposed
dynamic voltage frequency scaling(DVFS) states. We highlight the importance of
machine learning based approach in utilizing these multi-dimensional features
on a complex SoC, against profiling-based approaches. We also show that
learning the instantaneous frequency states polled from onboard frequency
driver (cpufreq) is adequate to identify a known application and flag
potentially malicious unknown application. The experimental results on
benchmarking applications running on ARMv8 processor in Snapdragon 820 board
demonstrates early detection of these apps, and atleast 85% accuracy in
detecting unknown applications. Overall, the highlight is to utilize a
low-complexity path to application inference attacks through learning
instantaneous frequency states pattern of CPU core
Adversarial Attack Based Countermeasures against Deep Learning Side-Channel Attacks
Numerous previous works have studied deep learning algorithms applied in the
context of side-channel attacks, which demonstrated the ability to perform
successful key recoveries. These studies show that modern cryptographic devices
are increasingly threatened by side-channel attacks with the help of deep
learning. However, the existing countermeasures are designed to resist
classical side-channel attacks, and cannot protect cryptographic devices from
deep learning based side-channel attacks. Thus, there arises a strong need for
countermeasures against deep learning based side-channel attacks. Although deep
learning has the high potential in solving complex problems, it is vulnerable
to adversarial attacks in the form of subtle perturbations to inputs that lead
a model to predict incorrectly.
In this paper, we propose a kind of novel countermeasures based on
adversarial attacks that is specifically designed against deep learning based
side-channel attacks. We estimate several models commonly used in deep learning
based side-channel attacks to evaluate the proposed countermeasures. It shows
that our approach can effectively protect cryptographic devices from deep
learning based side-channel attacks in practice. In addition, our experiments
show that the new countermeasures can also resist classical side-channel
attacks