Abstract: Many of the network security protocols employed today utilize symmetric block ciphers (DES, AES and CAST etc). The majority of the symmetric block ciphers implement the crucial substitution operation using look up tables, called substitution boxes. These structures should be highly nonlinear and have bit dispersal, i.e. avalanche, properties in order to render the cipher with resistant to cryptanalysis attempts, such as linear and differential cryptanalysis. Highly secure substitution boxes can be constructed using particular Boolean functions as components that have certain mathematical properties which enhance the robustness of the whole cryptoalgorithm. However, enforcing these properties on SBoxes is a highly computationally intensive task. In this paper, we present a distributed algorithm and its implementation on a computing cluster that accelerates the construction of secure substitution boxes with good security properties. It is fully parametric since it can employ any class of Boolean functions with algorithmically definable properties and can construct SBoxes of arbitrary sizes. We demonstrate the efficiency of the distributed algorithm implementation compared to its sequential counterpart, in a number of experiments.
Abstract: Clustering is a crucial network design approach to enable large-scale wireless sensor networks (WSNs) deployments. A large variety of clustering approaches has been presented focusing on different performance metrics. Such protocols usually aim at minimizing communication overhead, evenly distributing roles among the participating nodes, as well as controlling the network topology. Simulations on such protocols are performed using theoretical models that are based on unrealistic assumptions like the unit disk graph communication model, ideal wireless communication channels and perfect energy consumption estimations. With these assumptions taken for granted, theoretical models claim various performance milestones that cannot be achieved in realistic conditions. In this paper, we design a new clustering protocol that adapts to the changes in the environment and the needs and goals of the user applications. We address the issues that hinder its performance due to the real environment conditions and provide a deployable protocol. The implementation, integration and experimentation of this new protocol and it's optimizations, were performed using the \textsf{WISEBED} framework. We apply our protocol in multiple indoors wireless sensor testbeds with multiple experimental scenarios to showcase scalability and trade-offs between network properties and configurable protocol parameters. By analysis of the real world experimental output, we present results that depict a more realistic view of the clustering problem, regarding adapting to environmental conditions and the quality of topology control. Our study clearly demonstrates the applicability of our approach and the benefits it offers to both research \& development communities.
Abstract: This article presents a novel crawling and
clustering method for extracting and pro-
cessing cultural data from the web in a fully
automated fashion. Our architecture relies
upon a focused web crawler to download we
b documents relevant to culture. The
focused crawler is a web crawler that
searches and processes only those web pages
that are relevant to a particular topic. After downloading the pages, we extract from
each document a number of words for each th
ematic cultural area, filtering the docu-
ments with non-cultural content; we then create multidimensional document vectors
comprising the most frequent cultural term o
ccurrences. We calculate the dissimilarity
between the cultural-related document vect
ors and for each cultural theme, we use
clusteranalysis to partition the documents in
to a number of clusters. Our approach is
validated via a proof-of-concept applica
tion which analyzes hundreds of web pages
spanning different cultural thematic areas.
Abstract: This paper presents ongoing work on using data mining clustering to support the evaluation of software
systems' maintainability. As input for our analysis we employ software measurement data extracted from
Java source code. We propose a two-steps clustering process which facilitates the assessment of a system's
maintainability at rst, and subsequently an in-clusteranalysis in order to study the evolution of each
cluster as the system's versions pass by. The process is evaluated on Apache Geronimo, a J2EE 1.4 open
source Application Server. The evaluation involves analyzing several versions of this software system in
order to assess its evolution and maintainability over time. The paper concludes with directions for future
work.
Abstract: The existence of good probabilistic models for the job
arrival process and job characteristics is important for
the improved understanding of grid systems and the
prediction of their performance. In this study, we
present a thorough analysis of the job inter-arrival
times, the waiting times at the queues, the execution
times, and the data sizes exchanged at the
kallisto.hellasgrid.gr cluster, which is part of the
EGEE Grid infrastructure. By computing the Hurst
parameter of the inter-arrival times we find that the
job arrival process exhibits self-similarity/long-range
dependence. We also propose simple and intuitive
models for the job arrival process and the job
execution times. The models proposed were validated
and were found to be in very good agreement with our
empirical measurements.
Abstract: The existence of good probabilistic models
for the job arrival process and the delay components
introduced at different stages of job processing in a
Grid environment is important for the improved
understanding of the Grid computing concept. In this
study, we present a thorough analysis of the job
arrival process in the EGEE infrastructure and of the
time durations a job spends at different states in the
EGEE environment. We define four delay compo-
nents of the total job delay and model each compo-
nent separately. We observe that the job inter-arrival
times at the Grid level can be adequately modelled by
a rounded exponential distribution, while the total job
delay (from the time it is generated until the time it
completes execution) is dominated by the computing
element’s register and queuing times and the worker
node’s execution times. Further, we evaluate the
efficiency of the EGEE environment by comparing
the job total delay performance with that of a hypothetical ideal super-cluster and conclude that we
would obtain similar performance if we submitted the
same workload to a super-cluster of size equal to 34%
of the total average number of CPUs participating in
the EGEE infrastructure. We also analyze the job
inter-arrival times, the CE’s queuing times, the WN’s
execution times, and the data sizes exchanged at the
kallisto.hellasgrid.gr cluster, which is node in the
EGEE infrastructure. In contrast to the Grid level, we
find that at the cluster level the job arrival process
exhibits self-similarity/long-range dependence. Final-
ly, we propose simple and intuitive models for the job
arrival process and the execution times at the cluster
level.