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.     

Design and implementation of a database inference controller

The Inference Problem compromises database systems which are usually considered to be secure. here, users pose sets of queries and infer unauthorized information from the responses that they obtain. An Inference Controller is a device that prevents and/or detects security violations via inference. We are particularly interested in the inference problem which occurs in a multilevel operating environment. In such an environment, the users are cleared at different security levels and they access a multilevel database where the data is classified at different sensitivity levels. A multilevel secure database management system (MLS/DBMS) manages a multilevel database where its users cannot access data to which they are not authorized. However, providing a solution to the inference problem, where users issue multiple requests and consequently infer unauthorized knowledge is beyond the capability of currently available MLS/DBMSs. This paper describes the design and prototype development of an Inference Controller for a MLS/DBMS that functions during query processing. To our knowledge this is the first such inference controller prototype to be developed. We also describe some extensions to the inference controller so that an integrated solution can be provided to the problem.     

Semantic web-based social network access control

The existence of online social networks that include person specific information creates interesting opportunities for various applications ranging from marketing to community organization. On the other hand, security and privacy concerns need to be addressed for creating such applications. Improving social network access control systems appears as the first step toward addressing the existing security and privacy concerns related to online social networks. To address some of the current limitations, we have created an experimental social network using synthetic data which we then use to test the efficacy of the semantic reasoning based approaches we have previously suggested.     

A primer for understanding and applying data mining

Data mining is such a hot topic that it has become an obscured buzzword. Data mining can be a powerful tool for extracting useful information from tons of data. But it can just as easily extract erroneous and useless information if it's not used correctly. Key to avoiding the pitfalls is a basic understanding of what data mining is and what things to consider in planning a data mining project. The steps in a data mining project include: integrating and cleaning or modifying the data sources, mining the data, examining and pruning the mining results, and reporting the final results     

Adaptive Information Coding for Secure and Reliable Wireless Telesurgery Communications

Telesurgical Robot Systems (TRSs) have been the focus of research in academic, military, and commercial domains for many years. Contemporary TRSs address mission critical operations emerging in extreme fields such as battlefields, underwater, and disaster territories. The lack of wirelined communication infrastructure in such fields makes the use of wireless technologies including satellite and ad-hoc networks inevitable. TRSs over wireless environments pose unique challenges such as preserving a certain reliability threshold, adhering some maximum tolerable delay, and providing various security measures depending on the nature of the operation and communication environment. In this study, we present a novel approach that uses information coding to integrate both light-weight privacy and adaptive reliability in a single protocol called Secure and Statistically Reliable UDP (SSR-UDP). We prove that the offered security is equivalent to the existing AES-based long key crypto systems, yet, with significantly less computational overhead. Additionally, we demonstrate that the proposed scheme can meet high reliability and delay requirements of TRS applications in highly lossy environments while optimizing the bandwidth use. Our proposed SSR-UDP protocol can also be utilized in similar cyber-physical wireless application domains.     

Secure knowledge management: confidentiality, trust, and privacy

Knowledge management enhances the value of a corporation by identifying the assets and expertise as well as efficiently managing the resources. Security for knowledge management is critical as organizations have to protect their intellectual assets. Therefore, only authorized individuals must be permitted to execute various operations and functions in an organization. In this paper, secure knowledge management will be discussed, focusing on confidentiality, trust, and privacy. In particular, certain access-control techniques will be investigated, and trust management as well as privacy control for knowledge management will be explored.     

A scalable multi-level feature extraction technique to detect malicious executables

We present a scalable and multi-level feature extraction technique to detect malicious executables. We propose a novel combination of three different kinds of features at different levels of abstraction. These are binary n-grams, assembly instruction sequences, and Dynamic Link Library (DLL) function calls; extracted from binary executables, disassembled executables, and executable headers, respectively. We also propose an efficient and scalable feature extraction technique, and apply this technique on a large corpus of real benign and malicious executables. The above mentioned features are extracted from the corpus data and a classifier is trained, which achieves high accuracy and low false positive rate in detecting malicious executables. Our approach is knowledge-based because of several reasons. First, we apply the knowledge obtained from the binary n-gram features to extract assembly instruction sequences using our Assembly Feature Retrieval algorithm. Second, we apply the statistical knowledge obtained during feature extraction to select the best features, and to build a classification model. Our model is compared against other feature-based approaches for malicious code detection, and found to be more efficient in terms of detection accuracy and false alarm rate.