17,053 research outputs found
ANTIDS: Self-Organized Ant-based Clustering Model for Intrusion Detection System
Security of computers and the networks that connect them is increasingly
becoming of great significance. Computer security is defined as the protection
of computing systems against threats to confidentiality, integrity, and
availability. There are two types of intruders: the external intruders who are
unauthorized users of the machines they attack, and internal intruders, who
have permission to access the system with some restrictions. Due to the fact
that it is more and more improbable to a system administrator to recognize and
manually intervene to stop an attack, there is an increasing recognition that
ID systems should have a lot to earn on following its basic principles on the
behavior of complex natural systems, namely in what refers to
self-organization, allowing for a real distributed and collective perception of
this phenomena. With that aim in mind, the present work presents a
self-organized ant colony based intrusion detection system (ANTIDS) to detect
intrusions in a network infrastructure. The performance is compared among
conventional soft computing paradigms like Decision Trees, Support Vector
Machines and Linear Genetic Programming to model fast, online and efficient
intrusion detection systems.Comment: 13 pages, 3 figures, Swarm Intelligence and Patterns (SIP)- special
track at WSTST 2005, Muroran, JAPA
Search and Discovery Tools for Astronomical On-line Resources and Services
A growing number of astronomical resources and data or information services
are made available through the Internet. However valuable information is
frequently hidden in a deluge of non-pertinent or non up-to-date documents. At
a first level, compilations of astronomical resources provide help for
selecting relevant sites. Combining yellow-page services and meta-databases of
active pointers may be an efficient solution to the data retrieval problem.
Responses generated by submission of queries to a set of heterogeneous
resources are difficult to merge or cross-match, because different data
providers generally use different data formats: new endeavors are under way to
tackle this problem. We review the technical challenges involved in trying to
provide general search and discovery tools, and to integrate them through upper
level interfaces.Comment: 7 pages, 2 Postscript figures; to be published in A&A
Classifying Amharic News Text Using Self-Organizing Maps
The paper addresses using artificial neural networks for classification of Amharic news items. Amharic is the language for countrywide communication in Ethiopia and has its own writing system containing extensive systematic redundancy. It is quite dialectally diversified and probably representative of the languages of a continent that so far has received little attention within the language processing field.
The experiments investigated document clustering around user queries using Self-Organizing Maps, an unsupervised learning neural network strategy. The best ANN model showed a precision of 60.0% when trying to cluster unseen data, and a 69.5% precision when trying to classify it
Trace element contamination in the arms of the Danube Delta (Romania/Ukraine): Current state of knowledge and future needs
This paper provides the first critical synopsis of contamination by selected trace elements in the whole
Danube Delta (Romania/Ukraine) to: identify general patterns of contamination by trace elements across
the Delta, provide recommendations to refine existing monitoring networks and discuss the potential
toxicity of trace elements in the whole Delta. Sediment samples were collected between 2004 and 2007
in the three main branches of the Delta (Chilia, Sulina and Sfantu Gheorghe) and in the secondary delta of
the Chilia branch. Samples were analyzed for trace elements (Cd, Co, Cr, Cu, Ni, Pb, V, and Zn) and TiO2,
Fe2O3, MnO, CaCO3 and total organic carbon. Cluster analysis (CA) and Principal Component Analysis
(PCA) showed that levels of Cd, Cu, Pb, and Zn were influenced by anthropogenic activities. At the
opposite, concentrations of Cr and Ni largely originated from the weathering of rocks located in the
Romanian part of the Danube catchment and naturally rich in these elements. Data analysis using Self-
Organizing Maps confirmed the conclusions of CA/PCA and further detected that the contamination
tended to be higher in the Chilia and Sulina arms than in the Sfantu Gheorghe arm. The potential
ecological risks due to trace element contamination in the Danube Delta could be identified as moderate
and localized, provided that the presence of the natural sources of Cr and Ni was properly considered.
The available results suggest that monitoring sediment quality at the mouths of Sulina and Sfantu
Gheorghe arms is probably enough to get a picture of the sediment quality along their entire lengths.
However, a larger network of monitoring points is necessary in the Chilia and secondary Chilia delta to
account for the presence of local point sources and for the more complex hydrodynamic of this part of
the Danube Delta
Survey of data mining approaches to user modeling for adaptive hypermedia
The ability of an adaptive hypermedia system to create tailored environments depends mainly on the amount and accuracy of information stored in each user model. Some of the difficulties that user modeling faces are the amount of data available to create user models, the adequacy of the data, the noise within that data, and the necessity of capturing the imprecise nature of human behavior. Data mining and machine learning techniques have the ability to handle large amounts of data and to process uncertainty. These characteristics make these techniques suitable for automatic generation of user models that simulate human decision making. This paper surveys different data mining techniques that can be used to efficiently and accurately capture user behavior. The paper also presents guidelines that show which techniques may be used more efficiently according to the task implemented by the applicatio
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