16254824
Trust Propagation in Small Worlds
The possibility of a massive, networked infrastructure of diverse entities partaking in collaborative applications with each other increases more and more with the proliferation of mobile devices and the development of ad hoc networking technologies. In this context, traditional security measures do not scale well. We aim to develop trust-based security mechanisms using small world concepts to optimise formation and propagation of trust amongst entities in these vast networks. In this regard, we surmise that in a very large mobile ad hoc network, trust, risk, and recommendations can be propagated through relatively short paths connecting entities. Our work describes the design of trust-formation and risk-assessment systems, as well as that of an entity recognition scheme, within the context of the small world network topology.
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.
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.
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.
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.
16631254
Using data mining techniques and rough set theory for language modeling
In this article, we propose a new postprocessing strategy, word suggestion, based on a multiple word trigger-pair language model for Chinese character recognizers. With the word suggestion strategy, Chinese character recognizers may even achieve a recognition rate greater than the top-n candidate recognition rate. To construct the multiple word trigger-pair model, data mining techniques are used to alleviate the intensive computation problem. Furthermore, rough set theory is first used in the study to discover negatively correlated relationships between words in order to prevent introducing wrong words in the process of word suggestion.
27063773
Aspect-Oriented Middleware-Based Real-Time and Embedded Systems Software Process
Middleware facilitates the development of real-time and embedded systems. However, middleware itself is becoming increasingly complex. Using the methodology of aspect-oriented software development to analyze and reconstruct the architecture of middleware can make the architecture more modularized and more customizable. In addition, since middleware plays a significant role in the realtime and embedded systems development, its characteristics must be considered in the entire real-time and embedded systems software process from requirements analyze phase to testing phase. This paper provides two contributions to the study of real-time and embedded systems development. First, presents the problems of current middleware architecture and analyzes functional requirements and non-functional requirements of aspect-oriented middleware. Second, proposes an aspect-oriented middleware based real-time and embedded systems software process. Both functional and non-functional characteristics of middleware are considered in all phases of the software process.
19656381
Target Recognition of FLIR Images on Radial Basis Function Neural Network
The study of small target recognition in low SNR (Signal Noise Ratio) is the key problem about processing of forward-looking infrared (FLIR) images information. Eight features of objects based on IR radiation characteristics and wavelet-based are presented. These features are used to a radial basis function (RBF) network as input for learning and classification. The propose recognition algorithm is invariant to the translation, rotation, and scale channel of a shape. Experiments by real infrared images and noisy images are performed, and recognition results show that the method is very effective.
14185433
Affective Computing Model Based on Rough Fuzzy Sets
In human affective research, the fuzziness and roughness of the affective computing is the chief difficult. This paper builds a novel affective model based on rough fuzzy sets for the first time and presents a serial of static description results, which effectively solve the fuzzy and rough problems in affective computing. The model can realize the precision control of affective computing conveniently. Finally, the simulation shows that the results of the model for the affective static description are reasonable. This paper extends the results in (Yong Chen and Tong He, 2005) and lays the foundation of the affective computing in depth