Panoply: Low-TCB Linux applications with SGX enclaves Shinde et al., NDSS, 2017 Intel's Software Guard Extensions (SGX) supports a kind of reverse sandbox. With the normal sandbox model you're probably used to, we download untrusted code and run it in a trusted environment that we control. SGX supports running trusted code that you wrote, but … Continue reading Panoply: Low-TCB Linux applications with SGX enclaves
Year: 2017
Enlightening the I/O path: A holistic approach for application performance
Enlightening the I/O Path: A holistic approach for application performance Kim et al., FAST '17 Lots of applications contain a mix of foreground and background tasks. Since we're at the file system level here, for application, think Redis, MongoDB, PostgreSQL and so on. Typically user requests are considered foreground tasks, and tasks such as housekeeping, … Continue reading Enlightening the I/O path: A holistic approach for application performance
Chronix: Long term storage and retrieval technology for anomaly detection in operational data
Chronix: Long term storage and retrieval technology for anomaly detection in operational data Lautenschlager et al., FAST 2017 Chronix (http://www.chronix.io/ ) is a time-series database optimised to support anomaly detection. It supports a multi-dimensional generic time series data model and has built-in high level functions for time series operations. Chronix also a scheme called "Date-Delta-Compaction" (DDC) … Continue reading Chronix: Long term storage and retrieval technology for anomaly detection in operational data
MaMaDroid: Detecting Android malware by building Markov chains of behavorial models
MaMaDroid: Detecting Android malware by building Markov chains of behavioral models, Mariconti et al., NDSS 2017 Pick any security conference of your choosing, and you're sure to find plenty of papers examining the security of Android. It can paint a pretty bleak picture, but at the same time the Android ecosystem also seems to have … Continue reading MaMaDroid: Detecting Android malware by building Markov chains of behavorial models
Redundancy does not imply fault tolerance: analysis of distributed storage reactions to single errors and corruptions
Redundancy does not imply fault tolerance: analysis of distributed storage reactions to single errors and corruptions Ganesan et al., FAST 2017 It's a tough life being the developer of a distributed datastore. Thanks to the wonderful work of Kyle Kingsbury (aka, @aphyr) and his efforts on Jepsen.io, awareness of data loss and related issues in … Continue reading Redundancy does not imply fault tolerance: analysis of distributed storage reactions to single errors and corruptions
Thou shalt not depend on me: analysing the use of outdated JavaScript libraries on the web
Thou shalt not depend on me: analysing the use of outdated JavaScript libraries on the web Lauinger et al., NDSS 2017 Just based on the paper title alone, if you had to guess what the situation is with outdated JavaScript libraries on the web, you'd probably guess it was pretty bad. It turns out it's … Continue reading Thou shalt not depend on me: analysing the use of outdated JavaScript libraries on the web
HopFS: Scaling hierarchical file system metadata using NewSQL databases
HopFS: Scaling hierarchical file system metadata using NewSQL databases Niazi et al., FAST 2017 If you're working with big data and Hadoop, this one paper could repay your investment in The Morning Paper many times over (ok, The Morning Paper is free - but you do pay with your time to read it). You know … Continue reading HopFS: Scaling hierarchical file system metadata using NewSQL databases
RNN models for image generation
Today we're looking at the remaining papers from the unsupervised learning and generative networks section of the 'top 100 awesome deep learning papers' collection. These are: DRAW: A recurrent neural network for image generation, Gregor et al., 2015 Pixel recurrent neural networks, van den Oord et al., 2016 Auto-encoding variational Bayes, Kingma & Welling, 2014 … Continue reading RNN models for image generation
Unsupervised learning and GANs
Continuing our tour through some of the 'top 100 awesome deep learning papers,' today we're turning our attention to the unsupervised learning and generative networks section. I've split the papers here into two groups. Today we'll be looking at: Building high-level features using large-scale unsupervised learning, Le et al., 2012 Generative Adversarial Nets, Goodfellow et … Continue reading Unsupervised learning and GANs
Optimisation and training techniques for deep learning
Today we're looking at the 'optimisation and training techniques' section from the 'top 100 awesome deep learning papers' list. Random search for hyper-parameter optimization, Bergstra & Bengio 2012 Improving neural networks by preventing co-adaptation of feature detectors, Hinton et al., 2012 Dropout: a simple way to prevent neural networks from overfitting, Srivastava et al., 2014 … Continue reading Optimisation and training techniques for deep learning
the morning paper 