
<script type="text/javascript">
<!--
document.write('<div id="oa_widget"></div>');
document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=undefined&type=result"></script>');
-->
</script>handle: 2066/75491
We propose a new technique called Differential Cluster Analysis for side-channel key recovery attacks. This technique uses cluster analysis to detect internal collisions and it combines features from previously known collision attacks and Differential Power Analysis. It captures more general leakage features and can be applied to algorithmic collisions as well as implementation specific collisions. In addition, the concept is inherently multivariate. Various applications of the approach are possible: with and without power consumption model and single as well as multi-bit leakage can be exploited. Our findings are confirmed by practical results on two platforms: an AVR microcontroller with implemented DES algorithm and an AES hardware module. To our best knowledge, this is the first work demonstrating the feasibility of internal collision attacks on highly parallel hardware platforms. Furthermore, we present a new attack strategy for the targeted AES hardware module.
Technology, Science & Technology, Side-channel Cryptanalysis, AES, AES Hardware, cosic, Collision Attacks, Differential Power Analysis, Computer Science, Theory & Methods, Computer Science, Differential Cluster Analysis, Digital Security, CHANNEL COLLISION ATTACKS, Computer Science, Hardware & Architecture
Technology, Science & Technology, Side-channel Cryptanalysis, AES, AES Hardware, cosic, Collision Attacks, Differential Power Analysis, Computer Science, Theory & Methods, Computer Science, Differential Cluster Analysis, Digital Security, CHANNEL COLLISION ATTACKS, Computer Science, Hardware & Architecture
| citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 51 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
