Asserting beliefs in MLS relational models

A belief-consistent multilevel secure relational data model

The semantics of an extended referential integrity for a multilevel secure relational data model

A multilevel secure version of the entity/relationship (E/R) data model has been developed. Its multilevel secure properties are based on three principles: the granularity principle, the dependency principle, and the determinacy principle. These three principles are proposed as fundamental to the design of multilevel secure data models and databases. A comparison of the multilevel E/R model and the SeaView multilevel relational data model reveals that the SeaView model violates determinacy. >

A database management system (DBMS) should provide capabilities for defining and enforcing certain integrity constraints that must hold for the data recorded in the database. These constraints are derived from the semantics of the data and from the rules of the system under study (SuS) that database represents. Every DBMS is based on a data model that provides the means to achieve data abstraction and to express database schema. Each data model has a specific set of integrity constraints types. Some of constraint types are common for several data models and some of them are typical for a data model. Beyond well-known constraint types there are more complex ones that are difficult to express and enforce within the data model. In this paper we deal with the extended tuple constraint (ETC) type. It is the extension of tuple constraint that is one of constraint types of relational data model. In this paper we explain the usage of extended tuple constraint in relational data model and its implementation in relational DBMSs. XML data model does not recognize ETC type. We specify the extended tuple constraint in XML data model and propose two techniques for implementation of ETC in XML DBMSs. Each of them depends on the characteristics of a selected XML DBMS. We use XQuery functions in eXist DBMS, and triggers in Sedna DBMS.

This paper presents an extension to a Multi-Level Secure (MLS) data model that requires the classification of data and users into multiple security levels. In MLS systems, cover stories allow information provided to users at lower security levels to differ from information provided to users at higher security levels. Previous versions of the MLS model did not permit cover stories for key attributes, because the key is used to relate the various cover stories for a particular entity. We have extended the MLS model to include non-key related cover stories so that key attributes can also have different values at different security levels. In this paper we describe the necessary model changes and modifications to the relational algebra, which are required to implement non-key related cover stories. We demonstrate the improvements made by these changes and discuss the implementation and performance of a system based on the described concepts.

Intelligent approaches for security technologies

Presents an analysis of the use of the HyperText Transfer Protocol (HTTP) and other Web technologies for multi-level secure (MLS) systems that are connected to single-level network environments (e.g. Internet-like and intranet-like environments). Multiple single-level networks may be connected to these MLS systems. This analysis considers two examples of MLS systems. Known HTTP and Web security vulnerabilities are considered in the context of multi-level operations planned for an MLS database server to be accessed by Web browser software and for an MLS infrastructure supporting Web browsing on multiple Webs that each have a different security sensitivity level. The analysis focuses on the transfer of information across security boundaries where the security classification of information on one side of the boundary differs from that of the other side (a or transfer of information). The transfer of information is initiated by the Web browser (a network client) and the bulk of information transferred is data returned from the Web server. The analysis also focuses on threats from the less secure side of the boundary, including the threats of insertion of malicious code (e.g. virus or Trojan horse code) and denial-of-service attacks. The applications are referred to as the high-to-low example and the low-to-high example, denoting the direction of primary information flow.

Graph edit distance (GED) is an error tolerant graph matching paradigm whose methods are often evaluated in a classification context and less deeply assessed in terms of the accuracy of the found solution. To evaluate the accuracy of GED methods, low level information is required not only at the classification level but also at the matching level. Most of the publicly available repositories with associated ground truths are dedicated to evaluating graph classification or exact graph matching methods and so the matching correspondences as well as the distance between each pair of graphs are not directly evaluated. This paper consists of two parts. First, we provide a graph database repository annotated with low level information like graph edit distances and their matching correspondences. Second, we propose a set of performance evaluation metrics to assess the performance of GED methods.

This paper describes the issues related to the design of a trusted electronic document management system (TEDMS). The computer security issues and background research that lead to the final design of a TEDMS application are also discussed. The major component of an electronic document management system (EDMS) is the database in which the documents' metadata are kept. In a TEDMS, this database becomes a multilevel secure (MLS) database. The challenge of this design is to regroup MLS database design and trusted application design in one homogeneous application.

Since 1990, transaction processing in multilevel secure database management systems (DBMSs) has been receiving a great deal of attention from the database research community. Transaction processing in these systems requires modification of conventional scheduling algorithms and commit protocols. These modifications are necessary because preserving the usual transaction properties when transactions are executing at different security levels often conflicts with the enforcement of the security policy. Considerable effort has been devoted to the development of efficient, secure algorithms for the major types of secure DBMS architectures: kernelized, replicated, and distributed. An additional problem that arises uniquely in multilevel secure DBMSs is that of secure, correct execution when data at multiple security levels must be written within one transaction. Significant progress has been made in a number of these areas, and a few of the techniques have been incorporated into commercial trusted DBMS products. However, there are many open problems remain to be explored. This paper reviews the achievements to date in transaction processing for multilevel secure DBMSs. The paper provides an overview of transaction processing needs and solutions in conventional DBMSs as background, explains the constraints introduced by multilevel security, and then describes the results of research in multilevel secure transaction processing. Research results and limitations in concurrency control, multilevel transaction management, and secure commit protocols are summarized. Finally, important new areas are identified for secure transaction processing research.

Suggests the trusted server as a means of injecting multilevel security into applications such as command control and intelligence systems. It presents an example trusted database server and a trusted gateway server. A taxonomy is presented for gateway servers. Finally, the paper argues that the trusted server is a gap filler, in that it can off load much of the security design, maintenance and distribution complexity from client systems by permitting lower evaluation level clients to be used with the trusted server bearing much of the cost of multilevel security. >

A new class of ATR algorithms known in general as model-based have demonstrated some promising results. One major advantage of these new methods is that they are not only data-driven but they ore also model-driven. The fact that they are model-driven removes the dependence of the recognition process on a low level segmentation process. These new model-based approaches accomplish segmentation and recognition simultaneously. In other words, the process entertains multiple segmentation possibilities until a sufficient amount of high level and low level information is gathered so that an intelligent interpretation of what the image contains can be formed. A second major advantage of these new methodologies is the fact that the fusion of information from multiple sensors is a continual process not a one shot deal which is typical of first generation algorithms. This paper concentrates on the model-based ATR algorithm called relational template matching. >

Gliomas are the most common and highly growing tumors lead to high mortality rate in their highest grade. The early diagnosis of gliomas, and treatment planning are most important steps to enhance the life expectancy of a patient. Among the modern imaging techniques, magnetic resonance imaging (MRI) is the most robust and widely used technique to visualize the brain tumor. The CNN-based networks mainly depend on multi-branch and increasing the depth/width of the network to enhance the segmentation accuracy at the cost of high computational cost. To mitigate these drawbacks we therefore, propose a hybrid weights alignment with multi-dilated attention network for automatic brain tumor segmentation (Hybrid-DANet). It employs multiple modules incorporated on baseline encoder-decoder architecture. Firstly, we proposed a novel hybrid weight alignment with multi-dilated attention module (HWADA) is used between the skip connections. It has capability to obtain the different sets of aligned weight by using different dilation schemes. Different weight alignments play a vital role to obtain very precise targeted information while negating the less informative part. It utilizes the low and high level information with skip connections across each branch of encoder and decoder. Secondly, we incorporated a multi channel multi scale module (MCS) on the baseline module. It consists of multiple channels used to extract the channel-wise information with more reduced computational cost. To reduce the resultant saturated accuracy due to vanishing gradient problem, we incorporated the residual module (RM). Thus, the RM, and MCS are useful to obtain the deep, intrinsic, channel-wise feature without expansion of depth and height. Whereas the novel HWADA not only propagates the low level information but also process it to obtain more semantic features used for decoder. We have tested our proposed technique on well-known datasets; BraTS 2017, and 2018 with comparable performance to counter-part.

Automated detection and segmentation of cell nuclei is an essential step in breast cancer histopathology, so that there is improved accuracy, speed, level of automation and adaptability to new application. The goal of this paper is to develop efficient and accurate algorithms for detecting and segmenting cell nuclei in 2-D histological images. In this paper we will implement the utility of our nuclear segmentation algorithm in accurate extraction of nuclear features for automated grading of (a) breast cancer, and (b) distinguishing between cancerous and benign breast histology specimens. In order to address the issue the scheme integrates image information across three different scales: (1) low level information based on pixel values, (2) high-level information based on relationships between pixels for object detection, and(3)domain-specific information based on relationships between histological structures. Low-level information is utilized by a Bayesian Classifier to generate likelihood that each pixel belongs to an object of interest. High-level information is extracted in two ways: (i) by a level-set algorithm, where a contour is evolved in the likelihood scenes generated by the Bayesian classifier to identify object boundaries, and (ii) by a template matching algorithm, where shape models are used to identify glands and nuclei from the low-level likelihood scenes. Structural constraints are imposed via domain specific knowledge in order to verify whether the detected objects do indeed belong to structures of interest. The efficiency of our segmentation algorithm is evaluated by comparing breast cancer grading and benign vs. cancer discrimination accuracies with corresponding accuracies obtained via manual detection and segmentation of glands and nuclei.

The goal of this study was to examine whether there are two distinct groups of adolescent alcohol users: (1) low-level adolescent alcohol users who may be motivated by social status and (2) high-level users who are expected to be more concerned with coping. Social status is measured by the sociometric variables popularity and centrality. Variables that indicate a need to cope are depression, anxiety, and low self-esteem. Using waves I and II of the National Longitudinal Study of Adolescent Health (Add Health), support for these different etiological processes are found across high and lower level alcohol users. Among more normative (low level) alcohol users, social status plays a stronger role in predicting alcohol use than for more problematic (high level) alcohol users. For the high-level group, alcohol use appears to be a way to cope with depression. However, lower levels of self-esteem are only related to increased alcohol use among adolescents in the low-level group.