基于完全虚拟化的安全监控技术探析

近几年来随着计算机虚拟化技术的迅速发展,基于完全虚拟化技术的安全监控应运而生,它不仅能确保监控工具的有效性、牢固性,而且监控技术易于实现.本文将从安全监控架构中内核、内核可加戢模块两个方面存在的问题对基于完全虚拟化的安全监控技术进行探析.     

Detecting Driver Normal and Emergency Lane-Changing Intentions With Queuing Network-Based Driver Models

Driver intention detection is an important component in human-centric driver assistance systems. This article proposes a novel method for detecting driver normal and emergency left- or right-lane-changing intentions by using driver models based on the queuing network cognitive architecture. Driver lane-changing and lane-keeping models are developed and used to simulate driver behavior data associated with 5 kinds of intentions (i.e., normal and emergency left- or right-lane-changing and lane-keeping intentions). The differences between 5 sets of simulated behavior data and the collected actual behavior data are computed, and the intention associated with the smallest difference is determined as the detection outcome. The experimental results from 14 drivers in a driving simulator show that the method can detect normal and emergency lane-changing intentions within 0.325 s and 0.268 s of the steering maneuver onset, respectively, with high accuracy (98.27% for normal lane changes and 90.98% for emergency lane changes) and low false alarm rate (0.294%).     

Dynamic behavior of the discrete‐time double integrator with saturated locally stabilizing linear state feedback laws

The main contribution of this paper is to completely characterize the dynamic behavior of the discrete‐time double integrator with a saturated locally stabilizing linear state feedback law. In continuous‐time setting, any linear state feedback control law that locally stabilizes the double integrator also globally stabilizes the system in the presence of actuator saturation. In discrete‐time setting, the equivalent of the double integrator does not have the same property. In this paper, we completely characterize the global behavior of saturated locally stabilizing linear state feedback laws for the discrete‐time double integrator. Copyright © 2012 John Wiley & Sons, Ltd.     

Research on social relations cognitive model of mobile nodes in Internet of Things

Interaction and communication between humans with smart mobile devices are a new trend of development in Internet of Things (IoT). With the powerful sensing capability of smart device and human mobility, various services could be provided by building a trusted chain between service requesters and suppliers. The cognition of social relations between mobile nodes is the basis of final mobile-aware services. It involves many decision factors, such as time, space and activity patterns. Using social network theory, a new cognitive model for social relations of mobile nodes in IoT is proposed. Firstly, nodes' social relations are reasoned and quantified from multiple perspectives based on the summary of social characteristics of mobile nodes and the definition of different decision factors. Then the location factor, interconnection factor, service evaluation factor and feedback aggregation factor are defined to solve the shortcomings in existing quantitative models. Finally, the weight distribution is set up by information entropy and rough set theory for these decision factors; it can overcome the shortage of traditional methods, in which the weight is set up by subjective ways and hence their dynamic adaptability is poor. We compare our cognitive model to existing models using MIT dataset by defining a variety of test indicators, such as network overall density (NOD), the degree center potential (DCP), the network distribution index (EI), etc. Simulation results show that, the cognitive model has better internal structure and significant validity in network analysis, and thus can provide mobile-aware service effectively in dynamic environment.     

A novel sequential watermark detection model for efficient traceback of secret network attack flows

Network watermarking schemes have been proposed to trace secret network attack flows transferred through stepping stones as well as anonymous channels. However, most existing network flow watermark detection techniques focus on a fixed sample size of network data to achieve the required accuracy. Irrespective of the uncertainty or information content of successive observations, such detection techniques will result in low efficiency of watermark detection. We herein propose a novel sequential watermark detection model (SWDM) supporting three sequential detectors for efficient traceback of network attack flows. By exploiting the sequential probability ratio test approach, we first propose the intuitive paired-intervals-based optimum watermark detector (POWD) and the single-interval-based optimum watermark detector (SOWD) under the assumption of known parameters of the observed attack flow. We then propose the sequential sign watermark detector (SSWD) that operates on two-level quantized observations for nonparametric watermark detection. Based on our SWDM model, a statistical analysis of sequential detectors, with no assumptions or limitations concerning the distribution of the timing of packets, proves their effectiveness despite traffic timing perturbations. The experiments using a large number of synthetically-generated SSH traffic flows demonstrate that there is a significant advantage in using our sequential watermark detectors based on the proposed SWDM model over the existing fixed sample size watermark detector (FSWD). Compared to the FSWD detector, the POWD detector achieves almost 28% savings in the average number of packets. Especially, given the required probability of detection errors, the SOWD detector and the SSWD detector can achieve almost 47% and 29% savings, respectively, in the average number of required packets, thus resulting in not only guaranteed rates of detection errors but also high efficiency of flow traceback.     

Distributed topology control algorithm on broadcasting in wireless sensor network

Topology control can enhance energy efficiency and prolong network lifetime for wireless sensor networks. Several studies that attempted to solve the topology control problem focused only on topology construction or maintenance. This work designs a novel distributed and reliable energy-efficient topology control (RETC) algorithm for topology construction and maintenance in real application environments. Particularly, many intermittent links and accidents may result in packet loss. A reliable topology can ensure connectivity and energy efficiency, prolonging network lifetime. Thus, in the topology construction phase, a reliable topology is generated to increase network reachable probability. In the topology maintenance phase, this work applies a novel dynamic topology maintenance scheme to balance energy consumption using a multi-level energy threshold. This topology maintenance scheme can trigger the topology construction algorithm to build a new network topology with high reachable probability when needed. Experimental results demonstrate the superiority of the RETC algorithm in terms of average energy consumption and network lifetime.     

Improving image tags by exploiting web search results

Automatic image tagging automatically assigns image with semantic keywords called tags, which significantly facilitates image search and organization. Most of present image tagging approaches are constrained by the training model learned from the training dataset, and moreover they have no exploitation on other type of web resource (e.g., web text documents). In this paper, we proposed a search based image tagging algorithm (CTSTag), in which the result tags are derived from web search result. Specifically, it assigns the query image with a more comprehensive tag set derived from both web images and web text documents. First, a content-based image search technology is used to retrieve a set of visually similar images which are ranked by the semantic consistency values. Then, a set of relevant tags are derived from these top ranked images as the initial tag set. Second, a text-based search is used to retrieve other relevant web resources by using the initial tag set as the query. After the denoising process, the initial tag set is expanded with other tags mined from the text-based search result. Then, an probability flow measure method is proposed to estimate the probabilities of the expanded tags. Finally, all the tags are refined using the Random Walk with Restart (RWR) method and the top ones are assigned to the query images. Experiments on NUS-WIDE dataset show not only the performance of the proposed algorithm but also the advantage of image retrieval and organization based on the result tags.     

A resource allocation policy for delay minimization in fetching capacitated feeds

As social media services such as Twitter and Facebook are gaining popularity, the amount of information published from those services is explosively growing. Most of them use feeds to facilitate distribution of a huge volume of content they publish. In this context, many users subscribe to feeds to acquire up-to-date information through feed aggregation services, and recent real-time search engines also increasingly utilize feeds to promptly find recent web content when it is produced. Accordingly, it is necessary for such services to effectively fetch feeds for minimizing fetching delay, while at the same time maximizing the number of fetched entries. Fetching delay is a time lag between entry publication and retrieval, which is primarily incurred by finiteness of fetching resources. In this paper, we consider a polling-based approach among the methods applicable to fetching feeds, which bases on a specific schedule for visiting feeds. While the existing polling-based approaches have focused on the allocation of fetching resources to feeds in order to either reduce the fetching delay or increase the number of fetched entries, we propose a resource allocation policy that can optimize both objectives. Extensive experiments have been carried out to evaluate the proposed model, in comparison with the existing alternative methods.     

An update-based step-wise optimal cache replacement for wireless data access

Many network applications requires access to most up-to-date information. An update event makes the corresponding cached data item obsolete, and cache hits due to obsolete data items become simply useless to those applications. Frequently accessed but infrequently updated data items should get higher preference while caching, and infrequently accessed but frequently updated items should have lower preference. Such items may not be cached at all or should be evicted from the cache to accommodate items with higher preference. In wireless networks, remote data access is typically more expensive than in wired networks. Hence, an efficient caching scheme considers both data access and update patterns can better reduce data transmissions in wireless networks. In this paper, we propose a step-wise optimal update-based replacement policy, called the Update-based Step-wise Optimal (USO) policy, for wireless data networks to optimize transmission cost by increasing effective hit ratio. Our cache replacement policy is based on the idea of giving preference to frequently accessed but infrequently updated data, and is supported by an analytical model with quantitative analysis. We also present results from our extensive simulations. We demonstrate that (1) the analytical model is validated by the simulation results and (2) the proposed scheme outperforms the Least Frequently Used (LFU) scheme in terms of effective hit ratio and communication cost.     

On the algebraic structure of binary lattice-valued fuzzy relations

From a general algebraic point of view, this paper aims at providing an algebraic analysis for binary lattice-valued relations based on lattice implication algebras—a kind of lattice-valued propositional logical algebra. By abstracting away from the concrete lattice-valued relations and the operations on them, such as composition and converse, the notion of lattice-valued relation algebra is introduced, LRA for short. The reduct of an LRA is a lattice implication algebra. Such an algebra generalizes Boolean relation algebras by general distributive lattices and can provide a fundamental algebraic theory for establishing lattice-valued first-order logic. Some important results are generalized from the classical case. The notion of cylindric filter is introduced and the generated cylindric filters are characterized.