Comparison of recovery requirements with investigation requirements for intrusion management systems

Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2002Includes bibliographical references (leaves: 52-54)Text in English; Abstract: Turkish and Englishix, 54 leavesComputer systems resources and all data contained in the system may need to be protected against the increasing number of unauthorized access, manipulation and malicious intrusions. This thesis is concerned with intrusion management systems and specially with their investigation and recovery subsystems. The goals of these systems are to investigate intrusion attempts and recover from intrusions as fast as possible. In order to achieve these goals me should observe the fact that some of the intrusion attempts will be eventually successful should be accepted and necessary precautions should be taken.After an intrusion has taken place, the focus should be on the assessment:looking at what damage has occurred, how it happened, what changes can be made to prevent such attacks in the future. In this thesis, requirements of investigation and recovery process are determined and related guidelines developed. The similarities and differences between these guidelines are explained

New features for sentiment analysis: Do sentences matter?

1st International Workshop on Sentiment Discovery from Affective Data 2012, SDAD 2012 - In Conjunction with ECML-PKDD 2012; Bristol; United Kingdom; 28 September 2012 through 28 September 2012In this work, we propose and evaluate new features to be used in a word polarity based approach to sentiment classification. In particular, we analyze sentences as the first step before estimating the overall review polarity. We consider different aspects of sentences, such as length, purity, irrealis content, subjectivity, and position within the opinionated text. This analysis is then used to find sentences that may convey better information about the overall review polarity. The TripAdvisor dataset is used to evaluate the effect of sentence level features on polarity classification. Our initial results indicate a small improvement in classification accuracy when using the newly proposed features. However, the benefit of these features is not limited to improving sentiment classification accuracy since sentence level features can be used for other important tasks such as review summarization.European Commission, FP7, under UBIPOL (Ubiquitous Participation Platform for Policy Making) Projec

An extension of ontology based databases to handle preferences

1th International Conference on Enterprise Information Systems; Milan; Italy; 6 May 2009 through 10 May 2009Ontologies have been defined to make explicit the semantics of data. With the emergence of the SemanticWeb, the amount of ontological data (or instances) available has increased. To manage such data, Ontology Based DataBases (OBDBs), that store ontologies and their instance data in the same repository have been proposed. These databases are associated with exploitation languages supporting description, querying, etc. on both ontologies and data. However, usually queries return a big amount of data that may be sorted in order to find the relevant ones. Moreover, in the current, few approaches considering user preferences when querying have been developed. Yet this problem is fundamental for many applications especially in the e-commerce domain. In this paper, we first propose an extension of an existing OBDB, called OntoDB through extension of their ontology model in order to support semantic description of preferences. Secondly, an extension of an ontology based query language, called OntoQL defined on OntoDB for querying ontological data with preferences is presented. Finally, an implementation of the proposed extensions are described

Metamodeling approach to preference management in the semantic Web

2008 AAAI Workshop; Chicago, IL; United States; 13 July 2008 through 14 July 2008Preference is a superiority state to determine the preferable or the superior of one entity, property or constraint to another from a specified selection set. Preference issue is heavily studied in Semantic Web research area. The existing preference management approaches only consider the importance of concepts for capturing users' interests. This paper presents a metamodeling approach to preference management. Preference meta model consists of concepts and semantic relations to represent users' interests. Users may have the same type preferences in different domains. Thus, metamodeling must be used to define similar preferences for interoperability in different domains. In this paper, preference meta model defines a general storage structure to manage different types of preferences for personalized applications. Copyright © 2008, Association for the Advancement of Artificial Intelligence

SU-Sentilab : a classification system for sentiment analysis in twitter

Sentiment analysis refers to automatically extracting the sentiment present in a given natural language text. We present our participation to the SemEval2013 competition, in the sentiment analysis of Twitter and SMS messages. Our approach for this task is the combination of two sentiment analysis subsystems which are combined together to build the final system. Both subsystems use supervised learning using features based on various polarity lexicon

Un modèle générique pour la capture de préférences dans les bases de données à base ontologique

Nowadays information systems manage huge amount of data. With the emergence of the Semantic Web, the amount of available ontological data (or instances) has increased. To allow personalized access to this information has become a crucial necessity. Users are overwhelmed by the numerous results provided in response to their requests. In order to be usable, these results must often be sorted and filtered. The capture and exploitation of user preferences have been proposed as a solution to this problem. However, the existing approaches usually define preferences for a particular application. Thus, it is difficult to share and reuse the handled preferences in other contexts. Our approach, which defines a sharable and generic model to represent user preferences, based on several models proposed in the Databases and the Semantic Web communities. It incorporates several types of preferences proposed in the literature, but are treated separately. Our idea is to address preferences of a modular way by linking them to ontologies for describing the semantics of the data, handled by the applications. It is thus to raise the treatment preferences of logic level (structure) to the ontological level. The novelty of our approach is that the defined preferences are attached to the ontologies, which describe the semantic of the data manipulated by the applications. The preference model is formally defined using the EXPRESS data modeling language, which ensures a free ambiguity definition. Moreover, the proposed model offers a persistence mechanism and a dedicated language; which is implemented using Ontology Based Databases (OBDB) system, that manages both ontologies and extended data instances, in order to support a semantic description of preferences. These databases are associated with explanation languages, supporting description, querying, etc. on both ontologies and data. Usually queries return a big amount of data that may be sorted in order to find the relevant ones. Moreover, in the current situation few approaches are considering user preferences, when querying has been developed. Yet this problem is fundamental for many applications, especially in the e-commerce domain. Our second approach, which defines preferences in terms of ontologies that describe the semantics of handled data, provides a mechanism for querying with preferences. Thus, an extension to existing ontology based query languages is proposed, for querying ontological data with preferences. The proposed extension has been implemented onto the OntoDB OBDB associated to the OntoQL query language and the approach is illustrated through a case study in the tourism domain.De nos jours, les systèmes d'information gèrent de volumineuses données. Avec l'avènement du Web Sémantique, la quantité de données ontologiques (ou instances) disponibles s'est accrue. Permettre un accès personnalisé à ces données est devenue cruciale. Les utilisateurs sont submergés par les nombreux résultats fournis en réponse à leurs requêtes. Pour être utilisable, ces résultats doivent être filtrées et ordonnées. La capture et l'exploitation des préférences utilisateurs ont été proposées comme une solution à ce problème. Cependant, les approches existantes définissent habituellement les préférences pour une application donnée. Il est ainsi difficile de partager et réutiliser dans d'autres contextes les préférences capturées. Nous proposons une approche basée sur plusieurs modèles proposés au sein des communautés Bases de Données et Web Sémantique. Elle définit un model partageable et générique pour représenter les préférences utilisateurs, et incorpore plusieurs types de préférences de la littérature qui sont traités de manière séparée. L'idée sous-jacente à notre approche est de traiter les préférences de manière modulaire en les liant aux ontologies qui décrivent la sémantique des données gérées par les applications. Ainsi leur prise en compte se fait au niveau ontologique et non au niveau logique des données. La nouveauté de l'approche est que les préférences définies sont attachées aux ontologies, qui décrivent la sémantique des données manipulées par les applications. Le modèle de préférence est formellement défini en utilisant le langage de modélisation des données EXPRESS de manière à éviter toute ambiguïté du modèle. Par ailleurs, le modèle proposé offre un mécanisme de persistance et un langage d'interrogation dédié. Il est implémenté en utilisant un système de Bases de Données à Base Ontologique (BDBO) qui permet de gérer à la fois les ontologies et les données instances. Ceci permet d'offrir une description sémantique des préférences. Nous avons étendu le modèle des BDBO afin de supporter la prise en compte des préférences. L'implémentation a été faite dans le cadre de la BDBO OntoDB pour laquelle nous avons étendu le langage d'interrogation associé OntoQL. L'approche est illustrée à travers un cas d'étude dans le domaine du tourisme

Comparison of recovery requirements with investigation requirements for intrusion management systems

Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2002Includes bibliographical references (leaves: 52-54)Text in English; Abstract: Turkish and Englishix, 54 leavesComputer systems resources and all data contained in the system may need to be protected against the increasing number of unauthorized access, manipulation and malicious intrusions. This thesis is concerned with intrusion management systems and specially with their investigation and recovery subsystems. The goals of these systems are to investigate intrusion attempts and recover from intrusions as fast as possible. In order to achieve these goals me should observe the fact that some of the intrusion attempts will be eventually successful should be accepted and necessary precautions should be taken.After an intrusion has taken place, the focus should be on the assessment:looking at what damage has occurred, how it happened, what changes can be made to prevent such attacks in the future. In this thesis, requirements of investigation and recovery process are determined and related guidelines developed. The similarities and differences between these guidelines are explained

Performance Comparison of Combined Collaborative Filtering Algorithms for Recommender Systems

36th Annual IEEE International Computer Software and Applications Conference Workshops, COMPSACW 2012; Izmir; Turkey; 16 July 2012 through 20 July 2012Recommender systems have a goal to make personalized recommendations by using filtering algorithms. Collaborative filtering (CF) is one of the most popular techniques for recommender systems. As usual, huge number of the datasets on the Internet increase the amount of time to work on data. This challenge enforces people to improve better algorithms for processing data with user preferences and recommending the most appropriate item to the users. In this paper, we analyze CF algorithms and present results for combined user-based/item-based CF algorithms for different size of datasets. Our goal is to show combined solution results using Loglikelihood, Spearman, Tanimoto and Pearson algorithms. The contribution is to describe which user based CF algorithms and user/item based combined CF algorithms perform better according to dataset, sparsity, execution time and k-neighborhood values. © 2012 IEEE

DEVELOPMENT OF A DATABASE SECURITY POLICY METADATA MODEL AND ITS APPLICATION

Data stored in the database must be protected by pre-defined security policies designed for systems. The security policies must be described with the worth of knowledge and related risks. In this paper, two different security policies are explained by "Generally Accepted System Security Principles". For these policies metadata and metamodels are created and related metamodels are applied by using XML Schema. Then, the structures of elements for each model are exposed. For the realized application, determination of the policy in the organization, for changing conditions rearrangement will be easy. Thus, with the changes made in the metamodels, related policies will be continues in the process and metamodels are moved between different systems