54,689 research outputs found
Security Toolbox for Detecting Novel and Sophisticated Android Malware
This paper presents a demo of our Security Toolbox to detect novel malware in
Android apps. This Toolbox is developed through our recent research project
funded by the DARPA Automated Program Analysis for Cybersecurity (APAC)
project. The adversarial challenge ("Red") teams in the DARPA APAC program are
tasked with designing sophisticated malware to test the bounds of malware
detection technology being developed by the research and development ("Blue")
teams. Our research group, a Blue team in the DARPA APAC program, proposed a
"human-in-the-loop program analysis" approach to detect malware given the
source or Java bytecode for an Android app. Our malware detection apparatus
consists of two components: a general-purpose program analysis platform called
Atlas, and a Security Toolbox built on the Atlas platform. This paper describes
the major design goals, the Toolbox components to achieve the goals, and the
workflow for auditing Android apps. The accompanying video
(http://youtu.be/WhcoAX3HiNU) illustrates features of the Toolbox through a
live audit.Comment: 4 pages, 1 listing, 2 figure
Refining Coarse-grained Spatial Data using Auxiliary Spatial Data Sets with Various Granularities
We propose a probabilistic model for refining coarse-grained spatial data by
utilizing auxiliary spatial data sets. Existing methods require that the
spatial granularities of the auxiliary data sets are the same as the desired
granularity of target data. The proposed model can effectively make use of
auxiliary data sets with various granularities by hierarchically incorporating
Gaussian processes. With the proposed model, a distribution for each auxiliary
data set on the continuous space is modeled using a Gaussian process, where the
representation of uncertainty considers the levels of granularity. The
fine-grained target data are modeled by another Gaussian process that considers
both the spatial correlation and the auxiliary data sets with their
uncertainty. We integrate the Gaussian process with a spatial aggregation
process that transforms the fine-grained target data into the coarse-grained
target data, by which we can infer the fine-grained target Gaussian process
from the coarse-grained data. Our model is designed such that the inference of
model parameters based on the exact marginal likelihood is possible, in which
the variables of fine-grained target and auxiliary data are analytically
integrated out. Our experiments on real-world spatial data sets demonstrate the
effectiveness of the proposed model.Comment: Appears in Proceedings of the Thirty-Third AAAI Conference on
Artificial Intelligence (AAAI 2019
Interface refactoring in performance-constrained web services
This paper presents the development of REF-WS an approach to enable a Web Service provider to reliably evolve their service through the application of refactoring transformations. REF-WS is intended to aid service providers, particularly in a reliability and performance constrained domain as it permits upgraded ’non-backwards compatible’ services to be deployed into a performance constrained network where existing consumers depend on an older version of the service interface. In order for this to be successful, the refactoring and message mediation needs to occur without affecting functional compatibility with the services’ consumers, and must operate within the performance overhead expected of the original service, introducing as little latency as possible. Furthermore, compared to a manually programmed solution, the presented approach enables the service developer to apply and parameterize refactorings with a level of confidence that they will not produce an invalid or ’corrupt’ transformation of messages. This is achieved through the use of preconditions for the defined refactorings
Requirements for multidisciplinary design of aerospace vehicles on high performance computers
The design of aerospace vehicles is becoming increasingly complex as the various contributing disciplines and physical components become more tightly coupled. This coupling leads to computational problems that will be tractable only if significant advances in high performance computing systems are made. Some of the modeling, algorithmic and software requirements generated by the design problem are discussed
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