5337517
Throughput Optimized SHA-1 Architecture Using Unfolding Transformation
In this paper, the authors analyze the theoretical delay bound of the SHA-1 algorithm and propose architectures to achieve high throughput hardware implementations which approach this bound. According to the results of FPGA implementations, 3,541 Mbps with a pipeline and 893 Mbps without a pipeline were achieved. Moreover, synthesis results using 0.18mum CMOS technology showed that 10.4 Gbps with a pipeline and 3.1 Gbps without a pipeline can be achieved. These results are much faster than previously published results. The high throughputs are due to the unfolding transformation, which reduces the number of required cycles for one block hash. The authors reduced the required number of cycles to 12 cycles for a 512 bit block and showed that 12 cycles is the optimal in our design
15483673
Process Isolation for Reconfigurable Hardware
One of the pillars of trust-worthy computing is process isolation, the ability to keep process data private from other processes running on the same device. While embedded operating systems provide isolation for the software part of these processes, there is no commonly accepted isolation mechanism for the hardware resources. As a result, systems may remain vulnerable to hardware-based attacks. This paper presents a secure coprocessor interface that extends the concept of process isolation into reconfigurable hardware. In the resulting coprocessor design, context information for different processes concurrently accessing the coprocessor is physically kept private. The coprocessor interface can handle context switches between different processes without assistance of the operating system. Because of this, reconfiguration of computation units can occur independent of the main processor. Moreover, it does so with greater efficiency than what is possible using software only.
2159703
High Speed Channel Coding Architectures for the Uncoordinated OR Channel
Though it promises high bandwidths, the optical medium is not popular in local area networks. This is because current optical networks do not offer the ease of use and setup that an uncoordinated multiple access network such as Ethernet offers. By careful design and implementation of high speed channel coding architectures, we show that it is possible for optical networks to exhibit these desirable properties while maintaining high optical transmission rates. This paper presents an interleaver-division multiple access (IDMA) architecture implemented with a rate 1/20, 64-state Viterbi decoder and a word-based interleaver. These structures allowed us to achieve optical data rates of 2Gbps in FPGA implementation and 5.4Gbps for 0.18mum ASIC implementation. The techniques presented can be adapted for other similar architectures
5074364
Modeling adoptability of secure BGP protocols
Despite the existence of several secure BGP routing protocols, there has been little progress to date on actual adoption. Although feasibility for widespread adoption remains the greatest hurdle for BGP security, there has been little quantitative research into what properties contribute the most to the adoptability of a security scheme. In this paper, we provide a model for assessing the adoptability of a secure BGP routing protocol. We perform this evaluation by simulating incentives compatible adoption decisions of ISPs on the Internet under a variety of assumptions. Our results include: (a) the existence of a sharp threshold, where, if the cost of adoption is below the threshold, complete adoption takes place, while almost no adoption takes place above the threshold; (b) under a strong attacker model, adding a single hop of path authentication to origin authentication yields similar adoptability characteristics as a full path security scheme; (c) under a weaker attacker model, adding full path authentication (e.g., via S-BGP [9]) significantly improves the adoptability of BGP security over weaker path security schemes such as soBGP [16]. These results provide insight into the development of more adoptable secure BGP protocols and demonstrate the importance of studying adoptability of protocols.
987821
Using Clustering Information for Sensor Network Localization
Sensor network localization continues to be an important research challenge. The goal of localization is to assign geographic coordinates to each node in the sensor network. Localization schemes for sensor network systems should work with inexpensive off-the-shelf hardware, scale to large networks, and also achieve good accuracy in the presence of irregularities and obstacles in the deployment area. 
 
We present a novel approach for localization that can satisfy all of these desired properties. Recent developments in sensor network clustering algorithms have resulted in distributed algorithms that produce highly regular clusters. We propose to make use of this regularity to inform our localization algorithm. The main advantages of our approach are that our protocol requires only three randomly-placed nodes that know their geographic coordinates, and does not require any ranging or positioning equipment (i.e., no signal strength measurement, ultrasound ranging, or directional antennas are needed). So far, only the DV-Hop localization mechanism worked with the same assumptions [1]. We show that our proposed approach may outperform DV-Hop in certain scenarios, in particular when there exist large obstacles in the deployment field, or when the deployment area is free of obstacles but the number of anchors is limited.
17538498
Reasoning about Bayesian Network Classifiers
Bayesian network classifiers are used in many fields, and one common class of classifiers are naive Bayes classifiers. In this paper, we introduce an approach for reasoning about Bayesian network classifiers in which we explicitly convert them into Ordered Decision Diagrams (ODDS), which are then used to reason about the properties of these classifiers. Specifically, we present an algorithm for converting any naive Bayes classifier into an ODD, and we show theoretically and experimentally that this algorithm can give us an ODD that is tractable in size cvcn given an intractable number of instances. Since ODDS are tractable representations of classifiers, our algorithm allows us to efficiently test the equivalence of two naive Bayes classifiers and characterize discrepancies between them. We also show a number of additional results including a count of distinct classifiers that can be induced by changing some CPT in a naive Bayes classifier, and the range of allowable changes to a CPT which keeps the current classifier unchanged.
2565301
Visualizing input and output analysis for simulation (VINOAS)
Abstract Input analysis which involves a large amount of statistical analysis is one of the major steps in simulation. A software package with a well-designed user-friendly graphical window interface has been developed to assist modelers in identifying the underlying statistical distribution of the input data. It integrates techniques for assessing sample independence, hypothesizing families of distributions, estimation of parameters, and performing goodness-of-fit tests in a graphical environment. This allows the modelers to use the package in an interactive way and efforts can be saved in specifying the input probability distribution.
5672538
When do Numbers Really Matter?
Common wisdom has it that small distinctions in the probabilities quantifying a belief network do not matter much for the results of probabilistic queries. Yet, one can develop realistic scenarios under which small variations in network probabilities can lead to significant changes in computed queries. A pending theoretical question is then to analytically characterize parameter changes that do or do not matter. In this paper, we study the sensitivity of probabilistic queries to changes in network parameters and prove some tight bounds on the impact that such parameters can have on queries. Our analytical results pinpoint some interesting situations under which parameter changes do or do not matter. These results are important for knowledge engineers as they help them identify influential network parameters. They also help explain some of the previous experimental results and observations with regards to network robustness against parameter changes.