Gustavo Isaza

Towards Ontology-Based Intelligent Model for Intrusion Detection and Prevention

Nowadays new intelligent techniques have been used to improve the intrusion detection process in distributed environments. This paper presents an approach to define an ontology model for representing intrusion detection and prevention events as well as a hybrid intelligent system based on clustering and Artificial Neuronal Networks for classification and pattern recognition. We have specified attacks signatures, reaction rules, asserts, axioms using Ontology Web Language with Description Logic (OWL-DL) with event communication and correlation integrated on Multi-Agent Systems, incorporating supervised and unsupervised models and generating intelligent reasoning.

An Intrusion Detection and Prevention Model Based on Intelligent Multi-Agent Systems, Signatures and Reaction Rules Ontologies

Distributed Intrusion Detection Systems (DIDS) have been integrated to other techniques to incorporate some degree of adaptability. For instance, IDS and intelligent techniques facilitate the automatic generation of new signatures that allow this hybrid approach to detect and prevent unknown attacks patterns. Additionally, agent based architectures offer capabilities such as autonomy, reactivity, pro-activity, mobility and rationality that are desirables in IDSs. This paper presents an intrusion detection and prevention model that integrates an intelligent multi-agent system. The knowledge model is designed and represented with ontological signature, ontology rule representation for intrusion detection and prevention, and event correlation.

Efecto de los extractos de Phenax rugosus, Tabebuia chrysantha, Althernantera illiamsii y Solanum dolichosepalum sobre el leucograma y la producción de anticuerpos en ratas

Se describen los efectos que los extractos hidroalcoholicos de Phenax rugosus (nombre vulgar: esparietaria), Tabebuia chrysantha (canahuate), Althernantera williamsii (sanguinaria) y Solanum dolichosepalum (frutillo) tienen sobre el leucograma y la produccion de anticuerpos en ratas. El estudio se efectuo en ratas inmunizadas con una suspension de globulos rojos de carnero al 20%. Se realizaron recuentos leucocitarios (neutrofilos, linfocitos, monocitos y eosinofilos) y medicion de los titulos de anticuerpos anti-globulos rojos de carnero para valorar la respuesta humoral. El tratamiento por 10 dias, administrando los extractos por via oral a dosis de 1 g/kg de peso, produjo cambios diversos -segun la especie vegetal en los valores del leucograma y en la produccion de anticuerpos, en comparacion con el grupo control. Los extractos hidroalcoholicos de Phenax rugosus produjeron aumentos estadisticamente significativos en el recuento total de leucocitos y en los linfocitos, monocitos y eosinofilos (p<0.05); no se presentaron cambios significativos en la produccion de anticuerpos en comparacion con el grupo control. Hubo una disminucion significativa (p<0.03) en los valores del hematocrito. La Tabebuia chrysantha produjo un incremento estadisticamente significativo (p<0.03) en los anticuerpos, pero no hubo cambios significativos en los otros valores. La Althernantera williamsii produjo cambios estadisticamente significativos en la disminucion del hematocrito (p< 0.03), sin cambios significativos en los otros valores. El Solanum dolichosepalum mostro un incremento significativo (p< 0.03) en el recuento leucocitario, en los linfocitos y los monocitos, pero no hubo ningun cambio con significancia estadistica en los otros parametros.

Intrusion Correlation Using Ontologies and Multi-agent Systems

This paper proposes an ontology model for representing intrusion detection events and prevention rules, integrating multiagent systems based on unsupervised and supervised techniques for classification, correlation and pattern recognition. The semantic model describes attacks signatures, reaction tasks, axioms with alerts communication and correlation; nevertheless we have developed the prevention architecture integrated with another security tools. This article focuses on the approach to incorporate semantic operations that facilitate alerts correlation process and providing the inference and reasoning to the ontology model.

Towards a Linked Open Data Model for Coffee Functional Relationships

The development of technologies for massively disclose genetic information stored within cells generated the genomics revolution, where everyday exponentially growing databases with the description of the chemical structure of novel genes obtained with the automated nucleic acid sequencing. Each of these sequences is necessary to assign a function, in a process known as gene annotation comparing information of unknown sequence with sequences previously studied in the laboratory. This will identify candidate genes associated with traits of interest such as disease susceptibility or resistance, adaptation to the environment or animal or plant production. Given the volume of information, it is necessary that this be presented in a structured way so that such comparisons can be performed by computer agents quickly and it is open to the possibility of generating new knowledge by finding features and novel relationships between genes. This paper aims to present a bioinformatic application for functional relations search of transcriptome for coffee genetic material provided by the Coffee Investigation Center (CENICAFE), using the Bio2RDF biological database, applying new standards for LOD (Linked Open data) necessary to communicate effectively with other reference databases already operating under the scheme or Semantic Web.

Construction of coffee transcriptome networks based on gene annotation semantics

Gene annotation is a process that encompasses multiple approaches on the analysis of nucleic acids or protein sequences in order to assign structural and functional characteristics to gene models. When thousands of gene models are being described in an organism genome, construction and visualization of gene networks impose novel challenges in the understanding of complex expression patterns and the generation of new knowledge in genomics research. In order to take advantage of accumulated text data after conventional gene sequence analysis, this work applied semantics in combination with visualization tools to build transcriptome networks from a set of coffee gene annotations. A set of selected coffee transcriptome sequences, chosen by the quality of the sequence comparison reported by Basic Local Alignment Search Tool (BLAST) and Interproscan, were filtered out by coverage, identity, length of the query, and e-values. Meanwhile, term descriptors for molecular biology and biochemistry were obtained along the Wordnet dictionary in order to construct a Resource Description Framework (RDF) using Ruby scripts and Methontology to find associations between concepts. Relationships between sequence annotations and semantic concepts were graphically represented through a total of 6845 oriented vectors, which were reduced to 745 non-redundant associations. A large gene network connecting transcripts by way of relational concepts was created where detailed connections remain to be validated for biological significance based on current biochemical and genetics frameworks. Besides reusing text information in the generation of gene connections and for data mining purposes, this tool development opens the possibility to visualize complex and abundant transcriptome data, and triggers the formulation of new hypotheses in metabolic pathways analysis.

Identification of a Putative Ganoderic Acid Pathway Enzyme in a Ganoderma Australe Transcriptome by Means of a Hidden Markov Model

Ganoderma australe is a fungus widely used as a traditional medicine mainly in Eastern countries, but not studied in silico at the genomic level. This species is probably related to other well characterized fungus with similar properties, which may facilitate gene finding through comparative molecular analysis using appropriated bioinformatics tools. This paper aims to present a preliminary analysis of a G. australe transcriptome through some computational biology techniques implementing Hidden Markov Models (HMM) in order to predict a key putative enzyme (lanosterol synthase, EC 5.4.99.7) involved in the metabolic pathway of triterpenoids of therapeutic interest. The findings suggest that the HMM approach results more efficient than traditional comparisons by homology based on methods of multiple sequences alignment. Here we report the first evidence of a putative lanosterol synthase protein being expressed in cell cultures of G. australe.

GITIRBio: A Semantic and Distributed Service Oriented-Architecture for Bioinformatics Pipeline.

The need to process large quantities of data generated from genomic sequencing has resulted in a difficult task for life scientists who are not familiar with the use of command-line operations or developments in high performance computing and parallelization. This knowledge gap, along with unfamiliarity with necessary processes, can hinder the execution of data processing tasks. Furthermore, many of the commonly used bioinformatics tools for the scientific community are presented as isolated, unrelated entities that do not provide an integrated, guided, and assisted interaction with the scheduling facilities of computational resources or distribution, processing and mapping with runtime analysis. This paper presents the first approximation of a Web Services platform-based architecture (GITIRBio) that acts as a distributed front-end system for autonomous and assisted processing of parallel bioinformatics pipelines that has been validated using multiple sequences. Additionally, this platform allows integration with semantic repositories of genes for search annotations. GITIRBio is available at: http://c-head.ucaldas.edu.co:8080/gitirbio.

Arquitecturas y modelos de programación en computación Grid

The permanent growth in parallel computing and their hardware and software architectures has generated new challenges for the software developers. The Grid Computing takes more relevance in problems that require solutions on multiprocessing schemes or great load balancing problems; consequently the programming models must adapt to these developments so that they take advantage of and they support the characteristics of parallelism, distribution, transparency, heterogeneity and performance.. This paper describes the main programming models and standards for Grid computing are analyzed, as well as some examples in the use of packages, libraries and frameworks are detailed that can be adapted in the computing described it.

Netlang: A software for the linguistic analysis of corpora by means of complex networks

To date there is no software that directly connects the linguistic analysis of a conversation to a network program. Networks programs are able to extract statistical information from data basis with information about systems of interacting elements. Language has also been conceived and studied as a complex system. However, most proposals do not analyze language according to linguistic theory, but use instead computational systems that should save time at the price of leaving aside many crucial aspects for linguistic theory. Some approaches to network studies on language do apply precise linguistic analyses, made by a linguist. The problem until now has been the lack of interface between the analysis of a sentence and its integration into the network that could be managed by a linguist and that could save the analysis of any language. Previous works have used old software that was not created for these purposes and that often produced problems with some idiosyncrasies of the target language. The desired interface should be able to deal with the syntactic peculiarities of a particular language, the options of linguistic theory preferred by the user and the preservation of morpho-syntactic information (lexical categories and syntactic relations between items). Netlang is the first program able to do that. Recently, a new kind of linguistic analysis has been developed, which is able to extract a complexity pattern from the speakers linguistic production which is depicted as a network where words are inside nodes, and these nodes connect each other by means of edges or links (the information inside the edge can be syntactic, semantic, etc.). The Netlang software has become the bridge between rough linguistic data and the network program. Netlang has integrated and improved the functions of programs used in the past, namely the DGA annotator and two scripts (ToXML.pl and Xml2Pairs.py) used for transforming and pruning data. Netlang allows the researcher to make accurate linguistic analysis by means of syntactic dependency relations between words, while tracking record of the nature of such syntactic relationships (subject, object, etc). The Netlang software is presented as a new tool that solve many problems detected in the past. The most important improvement is that Netlang integrates three past applications into one program, and is able to produce a series of file formats that can be read by a network program. Through the Netlang software, the linguistic network analysis based on syntactic analyses, characterized for its low cost and the completely non-invasive procedure aims to evolve into a sufficiently fine grained tool for clinical diagnosis in potential cases of language disorders.