8564766
Location-based pairwise key establishments for static sensor networks
Sensor networks are ideal candidates for applications such as target tracking and environment monitoring. Security in sensor networks is critical when there are potential adversaries. Establishment of pairwise keys is a fundamental security service, which forms the basis of other security services such as authentication and encryption. However, establishing pairwise keys in sensor networks is not a trivial task, particularly due to the resource constraints on sensors. This paper presents several techniques for establishing pairwise keys in static sensor networks. These techniques take advantage of the observation that in static sensor networks, although it is difficult to precisely pinpoint sensors' positions, it is often possible to approximately determine their locations. This paper presents a simple location-aware deployment model, and develops two pairwise key predistribution schemes, a closest pairwise keys predistribution scheme and a location-based pairwise keys scheme using bivariate polynomials, by taking advantage of sensors' expected locations. The analysis in this paper indicates that these schemes can achieve better performance if such location information is available and that the smaller the deployment error (i.e., the difference between a sensor's actual location and its expected location) is, the better performance they can achieve.
769378
Efficient Distribution of Key Chain Commitments for Broadcast Authentication in Distributed Sensor Networks
Broadcast authentication is a fundamental security service in distributed sensor networks. A scheme named $\mu$TESLA has been proposed for efficient broadcast authentication in such networks. However, $\mu$TESLA requires initial distribution of certain information based on unicast between the base station and each sensor node before the actual authentication of broadcast messages. Due to the limited bandwidth in wireless sensor networks, this initial unicast-based distribution severely limits the application of $\mu$TESLA in large sensor networks. This paper presents a novel technique to replace the unicast-based initialization with a broadcast-based one. As a result, $\mu$TESLA can be used in a sensor network with a large amount of sensors, as long as the message from the base station can reach these sensor nodes. This paper further explores several techniques that improve the performance, the robustness, as well as the security of the proposed method. The resulting protocol satisfies several nice properties, including low overhead, tolerance of message loss, scalability to large networks, and resistance to replay attacks as well as some known Denial of Service (DOS) attacks.
399636
Resilient Cluster Formation for Sensor Networks
Sensor nodes are often organized into clusters to facilitate many network operations such as data aggregation. Clustering has to be protected in hostile environments. Otherwise, an attacker can easily mislead a cluster-based application by attacking the clustering protocol. This paper proposes three techniques for resilient cluster formation. The simple neighbor validation provides a simple yet effective way to validate a sensor's neighbors; the priority-based selection organizes clusters basedon the sensor's priority of being a cluster head; and the centralized detection further enhances the security by detecting misbehaving nodes. Another appealing benefit of this protocol is that a sensor node can make a clustering decision immediately once the neighborhood information is available. This further increases the difficulty of attacking the clustering protocol. The analysis also shows that the proposed protocol is efficient and effective in dealing with attacks.
286417
Practical broadcast authentication in sensor networks
Broadcast authentication is a critical security service in sensor networks; it allows a sender to broadcast messages to multiple nodes in an authenticated way. /spl mu/TESLA and multi-level /spl mu/TESLA have been proposed to provide such services for sensor networks. However, none of these techniques are scalable in terms of the number of senders. Though multi-level /spl mu/TESLA schemes can scale up to large sensor networks (in terms of receivers), they either use substantial bandwidth and storage at sensor nodes, or require significant resources at senders to deal with DOS attacks. This paper presents efficient techniques to support a potentially large number of broadcast senders using /spl mu/TESLA instances as building blocks. The proposed techniques are immune to the DOS attacks. This paper also provides two approaches, a revocation tree based scheme and a proactive distribution based scheme, to revoke the broadcast authentication capability from compromised senders. The proposed techniques are implemented, and evaluated through simulation on TinyOS. The analysis and experiment show that these techniques are efficient and practical, and can achieve better performance than the previous approaches.
1535984
Establishing pairwise keys in distributed sensor networks
Pairwise key establishment is a fundamental security service in sensor networks; it enables sensor nodes to communicate securely with each other using cryptographic techniques. However, due to the resource constraints on sensors, it is infeasible to use traditional key management techniques such as public key cryptography and key distribution center (KDC). To facilitate the study of novel pairwise key predistribution techniques, this paper presents a general framework for establishing pairwise keys between sensors on the basis of a polynomial-based key predistribution protocol [2]. This paper then presents two efficient instantiations of the general framework: a random subset assignment key predistribution scheme and a grid-based key predistribution scheme. The analysis in this paper indicates that these two schemes have a number of nice properties, including high probability (or guarantee) to establish pairwise keys, tolerance of node captures, and low communication overhead. Finally, this paper presents a technique to reduce the computation at sensors required by these schemes.
10993158
Efficient and Distributed Access Control for Sensor Networks
Sensor networks are often used to sense the physical world and provide observations for various uses. In hostile environments, it is critical to control the network access to ensure the integrity, availability, and at times confidentiality of the sensor data. This paper develops efficient methods for distributed access control in sensor networks. The paper starts with a baseline approach, which provides a more flexible and efficient way to enforce access control when compared with previous solutions. This paper then extends the baseline approach to enable privilege delegation, which allows a user to delegate its privilege to other users without using a trusted server, and broadcast query, which allows a user to access the network at a large scale efficiently. The privilege delegation and broadcast query are very useful in practice; none of the current solutions can achieve these two properties.
2861585
Evaluation Challenges in Large-Scale Document Summarization
We present a large-scale meta evaluation of eight evaluation measures for both single-document and multi-document summarizers. To this end we built a corpus consisting of (a) 100 Million automatic summaries using six summarizers and baselines at ten summary lengths in both English and Chinese, (b) more than 10,000 manual abstracts and extracts, and (c) 200 Million automatic document and summary retrievals using 20 queries. We present both qualitative and quantitative results showing the strengths and draw-backs of all evaluation methods and how they rank the different summarizers.
12746246
JRV: an interactive tool for data mining visualization
In this paper, we demonstrate JRV, a new data mining visualization tool for the knowledge discovery process where the user and computer can cooperate with each other. First, the computer can be instructed by the user interactively to compute values of several evaluation functions. Then, the user can take advantage of domain knowledge and assess the intermediate results obtained. Furthermore, by providing effective and efficient data visualization, the pattern recognition capacities of users can be greatly improved. Instead of being limited to two attributes at a given time in independence diagrams, this novel tool will allow simultaneous analyses of multiple attribute dependencies using four different drawing panels. Also, by utilizing the existing techniques of data visualization, we design a general model which can handle both categorical and numerical attributes in an intuitive way. With this model, we can identify patterns of interests efficiently. Through actual examples, we show that it might help users to find novel attribute relationships. This work is supported by NIH grant #RO1-CA98932-01.
215202
Neural network-based model reference adaptive control system
In this paper, an approach to model reference adaptive control based on neural networks is proposed and analyzed for a class of first-order continuous-time nonlinear dynamical systems. The controller structure can employ either a radial basis function network or a feedforward neural network to compensate adaptively the nonlinearities in the plant. A stable controller-parameter adjustment mechanism, which is determined using the Lyapunov theory, is constructed using a sigma-modification-type updating law. The evaluation of control error in terms of the neural network learning error is performed. That is, the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the neural network. In the design and analysis of neural network-based control systems, it is important to take into account the neural network learning error and its influence on the control error of the plant. Simulation results showing the feasibility and performance of the proposed approach are given.
11260681
Mining Multi-dimensional Data with Visualization Techniques
This paper describes a method to generate classification rules by using an interactive multidimensional data visualization and classification tool, called PolyCluster. PolyCluster is a system that adopts state-of-the-art algorithms for data visualization and integrates human domain knowledge into the construction process of classification rules. In addition, PolyCluster proposes a pair of novel and robust measurements, called the Average External Connecting Distance and the Average Internal Connecting Distance to evaluate the quality of the induced clusters. Experimental evaluation shows that PolyCluster is a visual-based approach that offers numerous improvements over previous visual-based techniques.
1870907
Macro-modeling concepts for the chip electrical interface
The power delivery network is made up of passive elements in the distribution network, as well as the active transistor loads. A chip typically has three types of power supplies that require attention: core, I/O, and analog. Core circuits consist of digital circuits and have the largest current demand. In addition to all of the system issues/models for the core, modeling the I/O subsystem has the additional requirement of modeling return paths and discontinuities. The analog circuits present yet a different challenge to the macro-modeling of the supply network because they place a tight demand on supply variations. This paper presents a design methodology on how to generate macro-models of the entire chip electrical interface. This methodology can be used by the chip, package, and system designers and is being used to design high-reliability servers.