7,754 research outputs found
Convolutional neural networks: a magic bullet for gravitational-wave detection?
In the last few years, machine learning techniques, in particular
convolutional neural networks, have been investigated as a method to replace or
complement traditional matched filtering techniques that are used to detect the
gravitational-wave signature of merging black holes. However, to date, these
methods have not yet been successfully applied to the analysis of long
stretches of data recorded by the Advanced LIGO and Virgo gravitational-wave
observatories. In this work, we critically examine the use of convolutional
neural networks as a tool to search for merging black holes. We identify the
strengths and limitations of this approach, highlight some common pitfalls in
translating between machine learning and gravitational-wave astronomy, and
discuss the interdisciplinary challenges. In particular, we explain in detail
why convolutional neural networks alone cannot be used to claim a statistically
significant gravitational-wave detection. However, we demonstrate how they can
still be used to rapidly flag the times of potential signals in the data for a
more detailed follow-up. Our convolutional neural network architecture as well
as the proposed performance metrics are better suited for this task than a
standard binary classifications scheme. A detailed evaluation of our approach
on Advanced LIGO data demonstrates the potential of such systems as trigger
generators. Finally, we sound a note of caution by constructing adversarial
examples, which showcase interesting "failure modes" of our model, where inputs
with no visible resemblance to real gravitational-wave signals are identified
as such by the network with high confidence.Comment: First two authors contributed equally; appeared at Phys. Rev.
LIGO detector characterization in the second and third observing runs
The characterization of the Advanced LIGO detectors in the second and third observing runs has increased the sensitivity of the instruments, allowing for a higher number of detectable gravitational-wave signals, and provided confirmation of all observed gravitational-wave events. In this work, we present the methods used to characterize the LIGO detectors and curate the publicly available datasets, including the LIGO strain data and data quality products. We describe the essential role of these datasets in LIGO–Virgo Collaboration analyses of gravitational-waves from both transient and persistent sources and include details on the provenance of these datasets in order to support analyses of LIGO data by the broader community. Finally, we explain anticipated changes in the role of detector characterization and current efforts to prepare for the high rate of gravitational-wave alerts and events in future observing runs
Practical Attacks Against Graph-based Clustering
Graph modeling allows numerous security problems to be tackled in a general
way, however, little work has been done to understand their ability to
withstand adversarial attacks. We design and evaluate two novel graph attacks
against a state-of-the-art network-level, graph-based detection system. Our
work highlights areas in adversarial machine learning that have not yet been
addressed, specifically: graph-based clustering techniques, and a global
feature space where realistic attackers without perfect knowledge must be
accounted for (by the defenders) in order to be practical. Even though less
informed attackers can evade graph clustering with low cost, we show that some
practical defenses are possible.Comment: ACM CCS 201
Are Delayed Issues Harder to Resolve? Revisiting Cost-to-Fix of Defects throughout the Lifecycle
Many practitioners and academics believe in a delayed issue effect (DIE);
i.e. the longer an issue lingers in the system, the more effort it requires to
resolve. This belief is often used to justify major investments in new
development processes that promise to retire more issues sooner.
This paper tests for the delayed issue effect in 171 software projects
conducted around the world in the period from 2006--2014. To the best of our
knowledge, this is the largest study yet published on this effect. We found no
evidence for the delayed issue effect; i.e. the effort to resolve issues in a
later phase was not consistently or substantially greater than when issues were
resolved soon after their introduction.
This paper documents the above study and explores reasons for this mismatch
between this common rule of thumb and empirical data. In summary, DIE is not
some constant across all projects. Rather, DIE might be an historical relic
that occurs intermittently only in certain kinds of projects. This is a
significant result since it predicts that new development processes that
promise to faster retire more issues will not have a guaranteed return on
investment (depending on the context where applied), and that a long-held truth
in software engineering should not be considered a global truism.Comment: 31 pages. Accepted with minor revisions to Journal of Empirical
Software Engineering. Keywords: software economics, phase delay, cost to fi
Fake Malware Generation Using HMM and GAN
In the past decade, the number of malware attacks have grown considerably and, more importantly, evolved. Many researchers have successfully integrated state-of-the-art machine learning techniques to combat this ever present and rising threat to information security. However, the lack of enough data to appropriately train these machine learning models is one big challenge that is still present. Generative modelling has proven to be very efficient at generating image-like synthesized data that can match the actual data distribution. In this paper, we aim to generate malware samples as opcode sequences and attempt to differentiate them from the real ones with the goal to build fake malware data that can be used to effectively train the machine learning models. We use and compare different Generative Adversarial Networks (GAN) algorithms and Hidden Markov Models (HMM) to generate such fake samples obtaining promising results
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