10,016 research outputs found
Forensic Analysis of the ChatSecure Instant Messaging Application on Android Smartphones
We present the forensic analysis of the artifacts generated on Android
smartphones by ChatSecure, a secure Instant Messaging application that provides
strong encryption for transmitted and locally-stored data to ensure the privacy
of its users.
We show that ChatSecure stores local copies of both exchanged messages and
files into two distinct, AES-256 encrypted databases, and we devise a technique
able to decrypt them when the secret passphrase, chosen by the user as the
initial step of the encryption process, is known.
Furthermore, we show how this passphrase can be identified and extracted from
the volatile memory of the device, where it persists for the entire execution
of ChatSecure after having been entered by the user, thus allowing one to carry
out decryption even if the passphrase is not revealed by the user.
Finally, we discuss how to analyze and correlate the data stored in the
databases used by ChatSecure to identify the IM accounts used by the user and
his/her buddies to communicate, as well as to reconstruct the chronology and
contents of the messages and files that have been exchanged among them.
For our study we devise and use an experimental methodology, based on the use
of emulated devices, that provides a very high degree of reproducibility of the
results, and we validate the results it yields against those obtained from real
smartphones
Instant restore after a media failure
Media failures usually leave database systems unavailable for several hours
until recovery is complete, especially in applications with large devices and
high transaction volume. Previous work introduced a technique called
single-pass restore, which increases restore bandwidth and thus substantially
decreases time to repair. Instant restore goes further as it permits read/write
access to any data on a device undergoing restore--even data not yet
restored--by restoring individual data segments on demand. Thus, the restore
process is guided primarily by the needs of applications, and the observed mean
time to repair is effectively reduced from several hours to a few seconds.
This paper presents an implementation and evaluation of instant restore. The
technique is incrementally implemented on a system starting with the
traditional ARIES design for logging and recovery. Experiments show that the
transaction latency perceived after a media failure can be cut down to less
than a second and that the overhead imposed by the technique on normal
processing is minimal. The net effect is that a few "nines" of availability are
added to the system using simple and low-overhead software techniques
LIKWID Monitoring Stack: A flexible framework enabling job specific performance monitoring for the masses
System monitoring is an established tool to measure the utilization and
health of HPC systems. Usually system monitoring infrastructures make no
connection to job information and do not utilize hardware performance
monitoring (HPM) data. To increase the efficient use of HPC systems automatic
and continuous performance monitoring of jobs is an essential component. It can
help to identify pathological cases, provides instant performance feedback to
the users, offers initial data to judge on the optimization potential of
applications and helps to build a statistical foundation about application
specific system usage. The LIKWID monitoring stack is a modular framework build
on top of the LIKWID tools library. It aims on enabling job specific
performance monitoring using HPM data, system metrics and application-level
data for small to medium sized commodity clusters. Moreover, it is designed to
integrate in existing monitoring infrastructures to speed up the change from
pure system monitoring to job-aware monitoring.Comment: 4 pages, 4 figures. Accepted for HPCMASPA 2017, the Workshop on
Monitoring and Analysis for High Performance Computing Systems Plus
Applications, held in conjunction with IEEE Cluster 2017, Honolulu, HI,
September 5, 201
Sharing large data collections between mobile peers
New directions in the provision of end-user computing experiences mean that we need to determine the best way to share data between small mobile computing devices. Partitioning large structures so that they can be shared efficiently provides a basis for data-intensive applications on such platforms. In conjunction with such an approach, dictionary-based compression techniques provide additional benefits and help to prolong battery life
Optimal Control of Applications for Hybrid Cloud Services
Development of cloud computing enables to move Big Data in the hybrid cloud
services. This requires research of all processing systems and data structures
for provide QoS. Due to the fact that there are many bottlenecks requires
monitoring and control system when performing a query. The models and
optimization criteria for the design of systems in a hybrid cloud
infrastructures are created. In this article suggested approaches and the
results of this build.Comment: 4 pages, Proc. conf. (not published). arXiv admin note: text overlap
with arXiv:1402.146
Distributed Caching for Processing Raw Arrays
As applications continue to generate multi-dimensional data at exponentially increasing rates, fast analytics to extract meaningful results is becoming extremely important. The database community has developed array databases that alleviate this problem through a series of techniques. In-situ mechanisms provide direct access to raw data in the original format---without loading and partitioning. Parallel processing scales to the largest datasets. In-memory caching reduces latency when the same data are accessed across a workload of queries. However, we are not aware of any work on distributed caching of multi-dimensional raw arrays. In this paper, we introduce a distributed framework for cost-based caching of multi-dimensional arrays in native format. Given a set of files that contain portions of an array and an online query workload, the framework computes an effective caching plan in two stages. First, the plan identifies the cells to be cached locally from each of the input files by continuously refining an evolving R-tree index. In the second stage, an optimal assignment of cells to nodes that collocates dependent cells in order to minimize the overall data transfer is determined. We design cache eviction and placement heuristic algorithms that consider the historical query workload. A thorough experimental evaluation over two real datasets in three file formats confirms the superiority - by as much as two orders of magnitude - of the proposed framework over existing techniques in terms of cache overhead and workload execution time
Distributed Caching for Complex Querying of Raw Arrays
As applications continue to generate multi-dimensional data at exponentially
increasing rates, fast analytics to extract meaningful results is becoming
extremely important. The database community has developed array databases that
alleviate this problem through a series of techniques. In-situ mechanisms
provide direct access to raw data in the original format---without loading and
partitioning. Parallel processing scales to the largest datasets. In-memory
caching reduces latency when the same data are accessed across a workload of
queries. However, we are not aware of any work on distributed caching of
multi-dimensional raw arrays. In this paper, we introduce a distributed
framework for cost-based caching of multi-dimensional arrays in native format.
Given a set of files that contain portions of an array and an online query
workload, the framework computes an effective caching plan in two stages.
First, the plan identifies the cells to be cached locally from each of the
input files by continuously refining an evolving R-tree index. In the second
stage, an optimal assignment of cells to nodes that collocates dependent cells
in order to minimize the overall data transfer is determined. We design cache
eviction and placement heuristic algorithms that consider the historical query
workload. A thorough experimental evaluation over two real datasets in three
file formats confirms the superiority -- by as much as two orders of magnitude
-- of the proposed framework over existing techniques in terms of cache
overhead and workload execution time
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