  The increasing use of Internet-of-Things (IoT) devices for monitoring a wide
spectrum of applications, along with the challenges of "big data" streaming
support they often require for data analysis, is nowadays pushing for an
increased attention to the emerging edge computing paradigm. In particular,
smart approaches to manage and analyze data directly on the network edge, are
more and more investigated, and Artificial Intelligence (AI) powered edge
computing is envisaged to be a promising direction. In this paper, we focus on
Data Centers (DCs) and Supercomputers (SCs), where a new generation of
high-resolution monitoring systems is being deployed, opening new opportunities
for analysis like anomaly detection and security, but introducing new
challenges for handling the vast amount of data it produces. In detail, we
report on a novel lightweight and scalable approach to increase the security of
DCs/SCs, that involves AI-powered edge computing on high-resolution power
consumption. The method -- called pAElla -- targets real-time Malware Detection
(MD), it runs on an out-of-band IoT-based monitoring system for DCs/SCs, and
involves Power Spectral Density of power measurements, along with AutoEncoders.
Results are promising, with an F1-score close to 1, and a False Alarm and
Malware Miss rate close to 0%. We compare our method with State-of-the-Art MD
techniques and show that, in the context of DCs/SCs, pAElla can cover a wider
range of malware, significantly outperforming SoA approaches in terms of
accuracy. Moreover, we propose a methodology for online training suitable for
DCs/SCs in production, and release open dataset and code.

Machine Learning;Cryptography and Security;Signal Processing;Machine Learning