2275333
Risk probability estimating based on clustering
Ubiquitous computing environments are highly dynamic, with new unforeseen circumstances and constantly changing environments, which introduces new risks that cannot be assessed through traditional means of risk analysis. Mobile entities in a ubiquitous computing environment require the ability to perform an autonomous assessment of the risk incurred by a specific interaction with another entity in a given context. This assessment will allow a mobile entity to decide whether sufficient evidence exists to mitigate the risk and allow the interaction to proceed. Such evidence might include records of prior experiences, recommendations from a trusted entity or the reputation of the other entity. We propose a dynamic mechanism for estimating the risk probability of a certain interaction in a given environment using hybrid neural networks. We argue that traditional risk assessment models from the insurance industry do not directly apply to ubiquitous computing environments. Instead, we propose a dynamic mechanism for risk assessment, which is based on pattern matching, classification and prediction procedures. This mechanism uses an estimator of risk probability, which is based on the automatic clustering of defining features of the environment and the other entity, which helps avoid subjective judgments as much as possible.
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.
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.
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.
14066096
ADM-Padé technique for the nonlinear lattice equations
ADM-Pade technique is a combination of Adomian decomposition method (ADM) and Pade approximants. We solve two nonlinear lattice equations using the technique which gives the approximate solution with higher accuracy and faster convergence rate than using ADM alone. Bell-shaped solitary solution of Belov-Chaltikian (BC) lattice and kink-shaped solitary solution of the nonlinear self-dual network equations (SDNEs) are presented. Comparisons are made between approximate solutions and exact solutions to illustrate the validity and the great potential of the technique.
15267488
Data access history cache and associated data prefetching mechanisms
Data prefetching is an effective way to bridge the increasing performance gap between processor and memory. As computing power is increasing much faster than memory performance, we suggest that it is time to have a dedicated cache to store data access histories and to serve prefetching to mask data access latency effectively. We thus propose a new cache structure, named Data Access History Cache (DAHC), and study its associated prefetching mechanisms. The DAHC behaves as a cache for recent reference information instead of as a traditional cache for instructions or data. Theoretically, it is capable of supporting many well known history-based prefetching algorithms, especially adaptive and aggressive approaches. We have carried out simulation experiments to validate DAHC design and DAHC-based data prefetching methodologies and to demonstrate performance gains. The DAHC provides a practical approach to reaping data prefetching benefits and its associated prefetching mechanisms are proven more effective than traditional approaches.
35857755
A new general algebraic method with symbolic computation to construct new travelling wave solution for the (1 + 1)-dimensional dispersive long wave equation
With the aid of symbolic computation, a new algebraic method, named Riccati equation rational expansion (RERE) method, is devised for constructing multiple travelling wave solutions for nonlinear evolution equations (NEEs). Compared with most existing tanh methods for finding travelling wave solutions, the proposed method not only recover the results by most known algebraic methods, but also provides new and more general solutions. With the aid of symbolic computation, we choose (1+1)-dimensional dispersive long wave equation (DLWE) to illustrate our method. As a result, we obtain many types of solutions including rational form solitary wave solutions, triangular periodic wave solutions and rational wave solutions. The properties of the new solitary wave solutions are shown by some figures.
15212853
Core-aware memory access scheduling schemes
Multi-core processors have changed the conventional hardware structure and require a rethinking of system scheduling and resource management to utilize them efficiently. However, current multi-core systems are still using conventional single-core memory scheduling. In this study, we investigate and evaluate traditional memory access scheduling techniques, and propose a core-aware memory scheduling for multi-core environments. Since memory requests from the same source exhibit better locality, it is reasonable to schedule the requests by taking the source of the requests into consideration. Motivated from this principle of locality, we propose two core-aware policies based on traditional bank-first and row-first schemes. Simulation results show that the core-aware policies can effectively improve the performance. Compared with the bank-first and row-first policies, the proposed core-aware policies reduce the execution time of certain NAS Parallel Benchmarks by up to 20% in running the benchmarks separately, and by 11% in running them concurrently.