1,566 research outputs found
Understanding (Un)Written Contracts of NVMe ZNS Devices with zns-tools
Operational and performance characteristics of flash SSDs have long been
associated with a set of Unwritten Contracts due to their hidden, complex
internals and lack of control from the host software stack. These unwritten
contracts govern how data should be stored, accessed, and garbage collected.
The emergence of Zoned Namespace (ZNS) flash devices with their open and
standardized interface allows us to write these unwritten contracts for the
storage stack. However, even with a standardized storage-host interface, due to
the lack of appropriate end-to-end operational data collection tools, the
quantification and reasoning of such contracts remain a challenge. In this
paper, we propose zns.tools, an open-source framework for end-to-end event and
metadata collection, analysis, and visualization for the ZNS SSDs contract
analysis. We showcase how zns.tools can be used to understand how the
combination of RocksDB with the F2FS file system interacts with the underlying
storage. Our tools are available openly at
\url{https://github.com/stonet-research/zns-tools}
Understanding (Un)Written Contracts of NVMe ZNS Devices with zns-tools
Operational and performance characteristics of flash SSDs have long been associated with a set of Unwritten Contracts due to their hidden, complex internals and lack of control from the host software stack. These unwritten contracts govern how data should be stored, accessed, and garbage collected. The emergence of Zoned Namespace (ZNS) flash devices with their open and standardized interface allows us to write these unwritten contracts for the storage stack. However, even with a standardized storage-host interface, due to the lack of appropriate end-to-end operational data collection tools, the quantification and reasoning of such contracts remain a challenge. In this paper, we propose zns.tools, an open-source framework for end-to-end event and metadata collection, analysis, and visualization for the ZNS SSDs contract analysis. We showcase how zns.tools can be used to understand how the combination of RocksDB with the F2FS file system interacts with the underlying storage. Our tools are available openly at \url{https://github.com/stonet-research/zns-tools}
Undermining User Privacy on Mobile Devices Using AI
Over the past years, literature has shown that attacks exploiting the
microarchitecture of modern processors pose a serious threat to the privacy of
mobile phone users. This is because applications leave distinct footprints in
the processor, which can be used by malware to infer user activities. In this
work, we show that these inference attacks are considerably more practical when
combined with advanced AI techniques. In particular, we focus on profiling the
activity in the last-level cache (LLC) of ARM processors. We employ a simple
Prime+Probe based monitoring technique to obtain cache traces, which we
classify with Deep Learning methods including Convolutional Neural Networks. We
demonstrate our approach on an off-the-shelf Android phone by launching a
successful attack from an unprivileged, zeropermission App in well under a
minute. The App thereby detects running applications with an accuracy of 98%
and reveals opened websites and streaming videos by monitoring the LLC for at
most 6 seconds. This is possible, since Deep Learning compensates measurement
disturbances stemming from the inherently noisy LLC monitoring and unfavorable
cache characteristics such as random line replacement policies. In summary, our
results show that thanks to advanced AI techniques, inference attacks are
becoming alarmingly easy to implement and execute in practice. This once more
calls for countermeasures that confine microarchitectural leakage and protect
mobile phone applications, especially those valuing the privacy of their users
- …