8,155 research outputs found
Intelligent Management and Efficient Operation of Big Data
This chapter details how Big Data can be used and implemented in networking
and computing infrastructures. Specifically, it addresses three main aspects:
the timely extraction of relevant knowledge from heterogeneous, and very often
unstructured large data sources, the enhancement on the performance of
processing and networking (cloud) infrastructures that are the most important
foundational pillars of Big Data applications or services, and novel ways to
efficiently manage network infrastructures with high-level composed policies
for supporting the transmission of large amounts of data with distinct
requisites (video vs. non-video). A case study involving an intelligent
management solution to route data traffic with diverse requirements in a wide
area Internet Exchange Point is presented, discussed in the context of Big
Data, and evaluated.Comment: In book Handbook of Research on Trends and Future Directions in Big
Data and Web Intelligence, IGI Global, 201
Structured Review of Code Clone Literature
This report presents the results of a structured review of code clone literature. The aim of the review is to assemble a conceptual model of clone-related concepts which helps us to reason about clones. This conceptual model unifies clone concepts from a wide range of literature, so that findings about clones can be compared with each other
SkyDOT (Sky Database for Objects in the Time Domain): A Virtual Observatory for Variability Studies at LANL
The mining of Virtual Observatories (VOs) is becoming a powerful new method
for discovery in astronomy. Here we report on the development of SkyDOT (Sky
Database for Objects in the Time domain), a new Virtual Observatory, which is
dedicated to the study of sky variability. The site will confederate a number
of massive variability surveys and enable exploration of the time domain in
astronomy. We discuss the architecture of the database and the functionality of
the user interface. An important aspect of SkyDOT is that it is continuously
updated in near real time so that users can access new observations in a timely
manner. The site will also utilize high level machine learning tools that will
allow sophisticated mining of the archive. Another key feature is the real time
data stream provided by RAPTOR (RAPid Telescopes for Optical Response), a new
sky monitoring experiment under construction at Los Alamos National Laboratory
(LANL).Comment: to appear in SPIE proceedings vol. 4846, 11 pages, 5 figure
CloudJet4BigData: Streamlining Big Data via an Accelerated Socket Interface
Big data needs to feed users with fresh processing results and cloud platforms can be used to speed up big data applications. This paper describes a new data communication protocol (CloudJet) for long distance and large volume big data accessing operations to alleviate the large latencies encountered in sharing big data resources in the clouds. It encapsulates a dynamic multi-stream/multi-path engine at the socket level, which conforms to Portable Operating System Interface (POSIX) and thereby can accelerate any POSIX-compatible applications across IP based networks. It was demonstrated that CloudJet accelerates typical big data applications such as very large database (VLDB), data mining, media streaming and office applications by up to tenfold in real-world tests
XSS-FP: Browser Fingerprinting using HTML Parser Quirks
There are many scenarios in which inferring the type of a client browser is
desirable, for instance to fight against session stealing. This is known as
browser fingerprinting. This paper presents and evaluates a novel
fingerprinting technique to determine the exact nature (browser type and
version, eg Firefox 15) of a web-browser, exploiting HTML parser quirks
exercised through XSS. Our experiments show that the exact version of a web
browser can be determined with 71% of accuracy, and that only 6 tests are
sufficient to quickly determine the exact family a web browser belongs to
Automated Dynamic Firmware Analysis at Scale: A Case Study on Embedded Web Interfaces
Embedded devices are becoming more widespread, interconnected, and
web-enabled than ever. However, recent studies showed that these devices are
far from being secure. Moreover, many embedded systems rely on web interfaces
for user interaction or administration. Unfortunately, web security is known to
be difficult, and therefore the web interfaces of embedded systems represent a
considerable attack surface.
In this paper, we present the first fully automated framework that applies
dynamic firmware analysis techniques to achieve, in a scalable manner,
automated vulnerability discovery within embedded firmware images. We apply our
framework to study the security of embedded web interfaces running in
Commercial Off-The-Shelf (COTS) embedded devices, such as routers, DSL/cable
modems, VoIP phones, IP/CCTV cameras. We introduce a methodology and implement
a scalable framework for discovery of vulnerabilities in embedded web
interfaces regardless of the vendor, device, or architecture. To achieve this
goal, our framework performs full system emulation to achieve the execution of
firmware images in a software-only environment, i.e., without involving any
physical embedded devices. Then, we analyze the web interfaces within the
firmware using both static and dynamic tools. We also present some interesting
case-studies, and discuss the main challenges associated with the dynamic
analysis of firmware images and their web interfaces and network services. The
observations we make in this paper shed light on an important aspect of
embedded devices which was not previously studied at a large scale.
We validate our framework by testing it on 1925 firmware images from 54
different vendors. We discover important vulnerabilities in 185 firmware
images, affecting nearly a quarter of vendors in our dataset. These
experimental results demonstrate the effectiveness of our approach
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