570616
Least privilege and privilege deprivation: towards tolerating mobile sink compromises in wireless sensor networks
Mobile sinks are needed in many sensor network applications for efficient data collection, data querying, localized sensor reprogramming, identifying and revoking compromised sensors, and other network maintenance. Employing mobile sinks however raises a new security challenge: if a mobile sink is given too many privileges, it will become very attractive for attack and compromise. Using a compromised mobile sink, an adversary may easily bring down or even take over the sensor network. Thus, security mechanisms that can tolerate mobile sink compromises are essential. In this paper, based on the principle of least privilege, we first propose several efficient schemes to restrict the privilege of a mobile sink without impeding its capability of carrying out any authorized operations for an assigned task. To further reduce the possible damages caused by a compromised mobile sink, we then propose efficient message forwarding schemes for depriving the privilege assigned to a compromised mobile sink immediately after its compromise has been detected. Through detailed analysis and simulations, we show that our schemes are secure and efficient, and are highly practical for sensor networks consisting of the current generation of sensors.
7443832
An interleaved hop-by-hop authentication scheme for filtering of injected false data in sensor networks
Sensor networks are often deployed in unattended environments, thus leaving these networks vulnerable to false data injection attacks in which an adversary injects false data into the network with the goal of deceiving the base station or depleting the resources of the relaying nodes. Standard authentication mechanisms cannot prevent this attack if the adversary has compromised one or a small number of sensor nodes. In this paper, we present an interleaved hop-by-hop authentication scheme that guarantees that the base station will detect any injected false data packets when no more than a certain number t nodes are compromised. Further, our scheme provides an upper bound B for the number of hops that a false data packet could be forwarded before it is detected and dropped, given that there are up to t colluding compromised nodes. We show that in the worst case B is O(t/sup 2/). Through performance analysis, we show that our scheme is efficient with respect to the security it provides, and it also allows a tradeoff between security and performance.
1511154
Association-based similarity testing and its applications
This paper proposes a new similarity measure between basket datasets based on associations. The new measure is calculated from support counts using a formula inspired by information entropy. Experiments on both real and synthetic datasets show the effectiveness of the measure. This paper then investigates the applications of the similarity measure. It first studies the problem of finding a mapping between categorical database attribute sets using similarity measures. A generic approach for identifying such a mapping is proposed. The approach is implemented based on the similarity measure proposed in the paper and its performance has been evaluated and validated. Moreover, this paper also explores the applications of using the similarity measure to mine distributed datasets.
12277810
Fuzzy Cultural Algorithms with Evolutionary Programming
Cultural Algorithms represent computational models based upon principles of cultural evolution [1]. They consist of a population component, a belief space containing knowledge compiled from individual experience, and a communication protocol which controls the interaction between two components. This protocol consists of an acceptance function and an influence function. The acceptance function determines how individuals can affect the knowledge contained in the belief space. The influence function determines how that knowledge can affect the individuals in the population.
7606118
Parsing Images into Region and Curve Processes
Natura scenes consist of a wide variety of stochastic patterns. While many patterns are represented well by statistical models in two dimensional regions as most image segmentation work assume, some other patterns are fundamentally one dimensional and thus cause major problems in segmentation. We call the former region processes and the latter curve processes. In this paper, we propose a stochastic algorithm for parsing an image into a number of region and curve processes. The paper makes the following contributions to the literature. Firstly, it presents a generative rope model for the curve processes in the form of Hidden Markov Model (HMM). The hidden layer is a Markov chain with each element being an image vase selected from an over-complete basis, such as Difference of Gaussians (DOG) or Difference of Offset Gaussians (DOOG) at various scales and orientations. The rope model accounts for the geometric smoothness and photometric coherence of the curve processes. Secondly, it integrates both 2D region models, such as textures, splines etc with 1D curve models uner the Bayes framework. Because both region and curve models are generative, they compete to explain input images in a layered representation. Thirdl, it achieves global optimization by effective Markov chain Monte Carlo methods in the sense of maximizing a posterior probability. The Markov chain consists of reversivle jumps and diffusions driven by bottom up information. The algorithm is applied to real images with satisfactory results. We verify the results through random synthesis and compare them against segmentations with region processes only.
7423086
Prior Learning and Gibbs Reaction-Diffusion
This article addresses two important themes in early visual computation: it presents a novel theory for learning the universal statistics of natural images, and, it proposes a general framework of designing reaction-diffusion equations for image processing. We studied the statistics of natural images including the scale invariant properties, then generic prior models were learned to duplicate the observed statistics, based on minimax entropy theory. The resulting Gibbs distributions have potentials of the form U(I; /spl Lambda/, S)=/spl Sigma//sub /spl alpha/=1//sup k//spl Sigma//sub x,y//spl lambda//sup (/spl alpha/)/((F/sup (/spl alpha/)/*I)(x,y)) with S={F/sup (1)/, F/sup (2)/,...,F/sup (K)/} being a set of filters and /spl Lambda/={/spl lambda//sup (1)/(),/spl lambda//sup (2)/(),...,/spl lambda//sup (K)/()} the potential functions. The learned Gibbs distributions confirm and improve the form of existing prior models such as line-process, but, in contrast to all previous models, inverted potentials were found to be necessary. We find that the partial differential equations given by gradient descent on U(I; /spl Lambda/, S) are essentially reaction-diffusion equations, where the usual energy terms produce anisotropic diffusion, while the inverted energy terms produce reaction associated with pattern formation, enhancing preferred image features. We illustrate how these models can be used for texture pattern rendering, denoising, image enhancement, and clutter removal by careful choice of both prior and data models of this type, incorporating the appropriate features.
7400363
Empirical Studies on Multi-label Classification
In classic pattern recognition problems, classes are mutually exclusive by definition. However, in many applications, it is quite natural that some instances belong to multiple classes at the same time. In other words, these applications are multi-labeled, classes are overlapped by definition and each instance may be associated to multiple classes. In this paper, we present a comparative study on various multi-label approaches using both gene and scene data sets. We expect our research efforts provide useful insights on the relationships among various classifiers as well as various evaluation measures and shed lights on future research. Although there is no clear winner across various performance measures, SVM binary and multi-label ADTree perform better than the others on most counts. We then propose a meta-learning approach by combining SVM binary and ADTree. Our experiments demonstrate that the combined method can take the advantages of the single approaches