Stealing Machine Learning Parameters via Side Channel Power Attacks

2021 ◽  
Author(s):  
Shaya Wolf ◽  
Hui Hu ◽  
Rafer Cooley ◽  
Mike Borowczak
Keyword(s):  
Machine Learning ◽  
Side Channel ◽  
Channel Power ◽  
Power Attacks

scholarly journals How to fool a black box machine learning based side-channel security evaluation

2021 ◽  
Author(s):  
Charles-Henry Bertrand Van Ouytsel ◽  
Olivier Bronchain ◽  
Gaëtan Cassiers ◽  
François-Xavier Standaert
Keyword(s):  
Machine Learning ◽  
Black Box ◽  
Security Evaluation ◽  
Side Channel

IoT Checker Using Timing Side-Channel and Machine Learning

2021 ◽  
pp. 220-226
Author(s):  
Kratika Sahu ◽  
Rasika Kshirsagar ◽  
Surbhi Vasudeva ◽  
Taghreed Alzahrani ◽  
Nima Karimian
Keyword(s):  
Machine Learning ◽  
Side Channel

scholarly journals Side-Channel Power Resistance for Encryption Algorithms Using Implementation Diversity

Cryptography ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 13
Author(s):  
Ivan Bow ◽  
Nahome Bete ◽  
Fareena Saqib ◽  
Wenjie Che ◽  
Chintan Patel ◽  
...  
Keyword(s):  
Power Analysis ◽  
Side Channel ◽  
Side Channel Attacks ◽  
Dynamic Partial Reconfiguration ◽  
Channel Power ◽  
Diversity Techniques ◽  
Gate Arrays ◽  
Field Programmable ◽  
Programmable Gate Arrays ◽  
Encryption Algorithms

This paper investigates countermeasures to side-channel attacks. A dynamic partial reconfiguration (DPR) method is proposed for field programmable gate arrays (FPGAs)s to make techniques such as differential power analysis (DPA) and correlation power analysis (CPA) difficult and ineffective. We call the technique side-channel power resistance for encryption algorithms using DPR, or SPREAD. SPREAD is designed to reduce cryptographic key related signal correlations in power supply transients by changing components of the hardware implementation on-the-fly using DPR. Replicated primitives within the advanced encryption standard (AES) algorithm, in particular, the substitution-box (SBOX)s, are synthesized to multiple and distinct gate-level implementations. The different implementations change the delay characteristics of the SBOXs, reducing correlations in the power traces, which, in turn, increases the difficulty of side-channel attacks. The effectiveness of the proposed countermeasures depends greatly on this principle; therefore, the focus of this paper is on the evaluation of implementation diversity techniques.

scholarly journals Real-Time Detection for Cache Side Channel Attack using Performance Counter Monitor

2020 ◽  
Vol 10 (3) ◽  
pp. 984 ◽  
Author(s):  
Jonghyeon Cho ◽  
Taehun Kim ◽  
Soojin Kim ◽  
Miok Im ◽  
Taehyun Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Detection Method ◽  
Machine Learning Algorithms ◽  
Side Channel ◽  
Side Channel Attacks ◽  
Detection Techniques ◽  
Performance Counter ◽  
Secret Information ◽  
Real Time Detection

Cache side channel attacks extract secret information by monitoring the cache behavior of a victim. Normally, this attack targets an L3 cache, which is shared between a spy and a victim. Hence, a spy can obtain secret information without alerting the victim. To resist this attack, many detection techniques have been proposed. However, these approaches have limitations as they do not operate in real time. This article proposes a real-time detection method against cache side channel attacks. The proposed technique performs the detection of cache side channel attacks immediately after observing a variation of the CPU counters. For this, Intel PCM (Performance Counter Monitor) and machine learning algorithms are used to measure the value of the CPU counters. Throughout the experiment, several PCM counters recorded changes during the attack. From these observations, a detecting program was implemented by using these counters. The experimental results show that the proposed detection technique displays good performance for real-time detection in various environments.

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