21338568
Research on Risk Probability Estimating Using Fuzzy Clustering for Dynamic Security Assessment
Effective network security management requires assessment of inherently uncertain events and circumstances dynamically. This paper addresses the problems of risk probability assessment and presents an alternative approach for estimating probability of security risk associated with some interaction in ubiquitous computing. A risk probability assessment formula is proposed, and an estimating model adopting the Fuzzy C-Means clustering algorithm is presented. An experiment based on DARPA intrusion detection evaluation data is given to support the suggested approach and demonstrate the feasibility and suitability for use. The practices indicate that fuzzy clustering technique provides concepts and theoretical results that are valuable in formulating and solving problems in dynamic security assessment.
28565174
Trust Evolution Policies for Security in Collaborative Ad Hoc Applications
The vision of pervasive computing has introduced the notion of a vast, networked infrastructure of heterogeneous entities interact through collaborative applications, e.g., playing a multi-player online game on the way to work. This will require interactions between users who may be marginally known or completely unknown to each other, or in situations where complete information is unavailable. This introduces the problem of assigning access rights to such marginally known or unknown entities. Explicit trust management has emerged as a solution to the problem of dealing with partial information about other users and the context in which the interaction takes place. We have implemented an access control mechanism based on the human notion of trust, where recommendations or initial participation in low risk interactions will allow entities to slowly build trust in each other. As the trust between two entities grows, interactions that entail a higher degree of risk may be allowed to proceed. We have used this mechanism in a simple role-based access control mechanism that uses trust to assign roles to users in a distributed blackjack card game application. This application allows us to experiment with different policies for trust-based admission control and trust evolution. In this paper we present an evaluation of policies specifying trust dynamics, which shows that our prototype reacts appropriately to the behaviour of other users and that the system updates trust values and implements admission policies in a manner similar to what would be expected from human trust assessment. This indicates that trust evolution policies can replace explicit human intervention in application scenarios that are similar to the evaluated prototype.
14925300
A Taxonomy of Data Prefetching Mechanisms
Data prefetching has been considered an effective way to mask data access latency caused by cache misses and to bridge the performance gap between processor and memory. With hardware and/or software support, data prefetching brings data closer to a processor before it is actually needed. Many prefetching techniques have been proposed in the last few years to reduce data access latency by taking advantage of multi-core architectures. In this paper, we propose a taxonomy that classifies various design concerns in developing a prefetching strategy. We discuss various prefetching strategies and issues that have to be considered in designing a prefetching strategy for multi-core processors.
6944670
Quality-oriented-maintenance for multiple interactive tooling components in discrete manufacturing processes
In discrete manufacturing processes such as stamping, assembly, or machining processes, product quality, often defined in terms of the dimensional integrity of work pieces, is jointly affected by multiple process variables. During the production phase, the states of tooling components, which are measured by adjustable process variables, are subject to possible random continuous drifts in their means & variances. These drifts of component states may significantly deteriorate product quality during production. Therefore, maintenance of the tooling components with consideration of both their continuous state drifts as well as catastrophic failures is crucial in assuring desired product quality & productivity. In contrast to traditional maintenance models where product quality has not been well addressed, especially for discrete manufacturing processes, a general quality-oriented-maintenance methodology is proposed in this paper to minimize the overall production costs. In this research, the total production cost includes product quality loss due to process drifts, productivity loss due to catastrophic failures, and maintenance costs. The quality-oriented-maintenance model is built based on a response model linking process variables with multidimensional product quality. It can be obtained either from engineering analysis for specific processes, or from statistical design of experiments. Three typical multi-component maintenance models are investigated under the general quality-oriented-maintenance framework. A case study for a sheet-metal stamping process is presented to demonstrate the effectiveness of the proposed methodology.
46139740
Automatic Path-Oriented Test Data Generation Using a Multi-population Genetic Algorithm
Automatic path-oriented test data generation is an undecidable problem and genetic algorithm (GA) has been used to test data generation since 1992. In favor of MATLAB, a multi-population genetic algorithm (MPGA) was implemented, which selects individuals for free migration based on their fitness values. Applying MPGA to generating path-oriented test data generation is a new and meaningful attempt. After depicting how to transform path-oriented test data generation into an optimization problem, basic process flow of path-oriented test data generation using GA was presented. Using a triangle classifier as program under test, experimental results show that MPGA based approach can generate path-oriented test data more effectively and efficiently than simple GA based approach does.
37352290
Comparison of Two Fitness Functions for GA-Based Path-Oriented Test Data Generation
Automatic path-oriented test data generation is not only a crucial problem but also a hot issue in the research area of software testing today. As a robust metaheuritstic search method in complex spaces, genetic algorithm (GA) has been used to path-oriented test data generation since 1992 and outperforms other approaches. A fitness function based on branch distance (BDBFF) and another based on normalized extended Hamming distance (SIMILARITY) are both applied in GA-based path-oriented test data generation. To compare performance of these two fitness functions, a triangle classification program was chosen as the example. Experimental results show that BDBFF-based approach can generate path-oriented test data more effectively and efficiently than SIMILARITY- based approach does.
20327796
An effective algorithm of motif finding problem
Identification of the short DNA sequence motif, which serves as binding targets for transcription factors, is a fundamental problem in both computer science and molecular biology. Especially, finding the subtle motifs with variable gaps is more challenging. In this paper, a new algorithm is presented, which explores some new strategies. Based on a neighbourhood set concept, a new probability matrix is defined, which can capture the target motifs effectively. An iterative restart strategy is used, by which we can use several similar motifs' information to detect the real motif to demonstrate the effectiveness of our algorithm. We test it on several kinds of data and compare it with some other current representation algorithms. Simulation shows that the algorithm can effectively detect the subtle motifs.