A NEW VIEW OF INFORMATION MODELING

The information model was conceived to address the complexities of managing large volumes of data, processes, designs, and tools that are shared by many business users with differing requirements. Because an information model derives much of its features from data models, the distinction between information modeling and data modeling is sometimes unclear. One perspective is that information modeling is context dependent: when a model is viewed as a representation scheme for users to comprehend, it is an information model. When used as a representation scheme to be processed by a computer, it is a data model.     

A framework for a video analysis tool for suspicious event detection

This paper proposes a framework to aid video analysts in detecting suspicious activity within the tremendous amounts of video data that exists in today’s world of omnipresent surveillance video. Ideas and techniques for closing the semantic gap between low-level machine readable features of video data and high-level events seen by a human observer are discussed. An evaluation of the event classification and detection technique is presented and a future experiment to refine this technique is proposed. These experiments are used as a lead to a discussion on the most optimal machine learning algorithm to learn the event representation scheme proposed in this paper.

A new intrusion detection system using support vector machines and hierarchical clustering

Whenever an intrusion occurs, the security and value of a computer system is compromised. Network-based attacks make it difficult for legitimate users to access various network services by purposely occupying or sabotaging network resources and services. This can be done by sending large amounts of network traffic, exploiting well-known faults in networking services, and by overloading network hosts. Intrusion Detection attempts to detect computer attacks by examining various data records observed in processes on the network and it is split into two groups, anomaly detection systems and misuse detection systems. Anomaly detection is an attempt to search for malicious behavior that deviates from established normal patterns. Misuse detection is used to identify intrusions that match known attack scenarios. Our interest here is in anomaly detection and our proposed method is a scalable solution for detecting network-based anomalies. We use Support Vector Machines (SVM) for classification. The SVM is one of the most successful classification algorithms in the data mining area, but its long training time limits its use. This paper presents a study for enhancing the training time of SVM, specifically when dealing with large data sets, using hierarchical clustering analysis. We use the Dynamically Growing Self-Organizing Tree (DGSOT) algorithm for clustering because it has proved to overcome the drawbacks of traditional hierarchical clustering algorithms (e.g., hierarchical agglomerative clustering). Clustering analysis helps find the boundary points, which are the most qualified data points to train SVM, between two classes. We present a new approach of combination of SVM and DGSOT, which starts with an initial training set and expands it gradually using the clustering structure produced by the DGSOT algorithm. We compare our approach with the Rocchio Bundling technique and random selection in terms of accuracy loss and training time gain using a single benchmark real data set. We show that our proposed variations contribute significantly in improving the training process of SVM with high generalization accuracy and outperform the Rocchio Bundling technique.     

Access control for web data: models and policy languages

The web has made easily available an enormous amount of information in digital form and has facilitated the exchange of such information. In this environment, access control is a primary concern. The key issue is how to trade-off between maximizing the sharing of information and enforcing a controlled access to web data. In this paper we start by outlining which are the main access control requirements of web data. Then, we review researches carried on in the field, by mainly focusing on xml. Finally, we discuss policy languages for the semantic web, and outline which are the main research directions in this field.     

Selective and authentic third-party distribution of XML documents

Third-party architectures for data publishing over the Internet today are receiving growing attention, due to their scalability properties and to the ability of efficiently managing large number of subjects and great amount of data. In a third-party architecture, there is a distinction between the Owner and the Publisher of information. The Owner is the producer of information, whereas Publishers are responsible for managing (a portion of) the Owner information and for answering subject queries. A relevant issue in this architecture is how the Owner can ensure a secure and selective publishing of its data, even if the data are managed by a third-party, which can prune some of the nodes of the original document on the basis of subject queries and access control policies. An approach can be that of requiring the Publisher to be trusted with regard to the considered security properties. However, the serious drawback of this solution is that large Web-based systems cannot be easily verified to be secure and can be easily penetrated. For these reasons, we propose an alternative approach, based on the use of digital signature techniques, which does not require the Publisher to be trusted. The security properties we consider are authenticity and completeness of a query response, where completeness is intended with regard to the access control policies stated by the information Owner. In particular, we show that, by embedding in the query response one digital signature generated by the Owner and some hash values, a subject is able to locally verify the authenticity of a query response. Moreover, we present an approach that, for a wide range of queries, allows a subject to verify the completeness of query results.     

Recovery point selection on a reverse binary tree task model

An analysis is conducted of the complexity of placing recovery points where the computation is modeled as a reverse binary tree task model. The objective is to minimize the expected computation time of a program in the presence of faults. The method can be extended to an arbitrary reverse tree model. For uniprocessor systems, an optimal placement algorithm is proposed. For multiprocessor systems, a procedure for computing their performance is described. Since no closed form solution is available, an alternative measurement is proposed that has a closed form formula. On the basis of this formula, algorithms are devised for solving the recovery point placement problem. The estimated formula can be extended to include communication delays where the algorithm devised still applies     

Representation of one-one degrees by decision problems for system functions

In this paper the existence of a System function is shown: a kind of Combinatorial System defined by Cleave; such that arbitrary distinct recursively enumerable one-one degrees can be represented by distinct decision problems for this System function.     

Secure query-processing strategies

Strategies for secure query processing multilevel-security database management systems are proposed. They are carried out by query modification, a technique that has been used for enforcing integrity constraints and providing view mechanisms. The technique consists of replacing the query the user presents with one that, when evaluated, will perform the desired function. In the case of a view mechanism, the names of views referenced in the query are replaced by the definitions of the views in terms of base relations. The basic strategy and two variants-adding environmental information and using graphs-are described. The performance of the strategies is examined