43,760 research outputs found
Scalable dynamic information flow tracking and its applications
We are designing scalable dynamic information flow tracking techniques and employing them to carry out tasks related to debugging (bug location and fault avoidance), security (software attack detection), and data validation (lineage tracing of scientific data). The focus of our ongoing work is on developing online dynamic analysis techniques for long running multithreaded programs that may be executed on a single core or on multiple cores to exploit thread level parallelism. 1. 1
Scalable Approach to Uncertainty Quantification and Robust Design of Interconnected Dynamical Systems
Development of robust dynamical systems and networks such as autonomous
aircraft systems capable of accomplishing complex missions faces challenges due
to the dynamically evolving uncertainties coming from model uncertainties,
necessity to operate in a hostile cluttered urban environment, and the
distributed and dynamic nature of the communication and computation resources.
Model-based robust design is difficult because of the complexity of the hybrid
dynamic models including continuous vehicle dynamics, the discrete models of
computations and communications, and the size of the problem. We will overview
recent advances in methodology and tools to model, analyze, and design robust
autonomous aerospace systems operating in uncertain environment, with stress on
efficient uncertainty quantification and robust design using the case studies
of the mission including model-based target tracking and search, and trajectory
planning in uncertain urban environment. To show that the methodology is
generally applicable to uncertain dynamical systems, we will also show examples
of application of the new methods to efficient uncertainty quantification of
energy usage in buildings, and stability assessment of interconnected power
networks
A Novel Framework for Software Defined Wireless Body Area Network
Software Defined Networking (SDN) has gained huge popularity in replacing
traditional network by offering flexible and dynamic network management. It has
drawn significant attention of the researchers from both academia and
industries. Particularly, incorporating SDN in Wireless Body Area Network
(WBAN) applications indicates promising benefits in terms of dealing with
challenges like traffic management, authentication, energy efficiency etc.
while enhancing administrative control. This paper presents a novel framework
for Software Defined WBAN (SDWBAN), which brings the concept of SDN technology
into WBAN applications. By decoupling the control plane from data plane and
having more programmatic control would assist to overcome the current lacking
and challenges of WBAN. Therefore, we provide a conceptual framework for SDWBAN
with packet flow model and a future direction of research pertaining to SDWBAN.Comment: Presented on 8th International Conference on Intelligent Systems,
Modelling and Simulatio
I know what leaked in your pocket: uncovering privacy leaks on Android Apps with Static Taint Analysis
Android applications may leak privacy data carelessly or maliciously. In this
work we perform inter-component data-flow analysis to detect privacy leaks
between components of Android applications. Unlike all current approaches, our
tool, called IccTA, propagates the context between the components, which
improves the precision of the analysis. IccTA outperforms all other available
tools by reaching a precision of 95.0% and a recall of 82.6% on DroidBench. Our
approach detects 147 inter-component based privacy leaks in 14 applications in
a set of 3000 real-world applications with a precision of 88.4%. With the help
of ApkCombiner, our approach is able to detect inter-app based privacy leaks
Scalable Bayesian modeling, monitoring and analysis of dynamic network flow data
Traffic flow count data in networks arise in many applications, such as
automobile or aviation transportation, certain directed social network
contexts, and Internet studies. Using an example of Internet browser traffic
flow through site-segments of an international news website, we present
Bayesian analyses of two linked classes of models which, in tandem, allow fast,
scalable and interpretable Bayesian inference. We first develop flexible
state-space models for streaming count data, able to adaptively characterize
and quantify network dynamics efficiently in real-time. We then use these
models as emulators of more structured, time-varying gravity models that allow
formal dissection of network dynamics. This yields interpretable inferences on
traffic flow characteristics, and on dynamics in interactions among network
nodes. Bayesian monitoring theory defines a strategy for sequential model
assessment and adaptation in cases when network flow data deviates from
model-based predictions. Exploratory and sequential monitoring analyses of
evolving traffic on a network of web site-segments in e-commerce demonstrate
the utility of this coupled Bayesian emulation approach to analysis of
streaming network count data.Comment: 29 pages, 16 figure
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