Structured peer-to-peer overlay networks provide a sub-strate for the construction of large-scale, decentralized applications, including distributed storage, group com-munication, and content distribution. These overlays are highly resilient; they can route messages correctly even when a large fraction of the nodes crash or the network partitions. But current overlays are not secure; even a small fraction of malicious nodes can prevent correct message delivery throughout the overlay. This prob-lem is particularly serious in open peer-to-peer systems, where many diverse, autonomous parties without pre-existing trust relationships wish to pool their resources. This paper studies attacks aimed at preventing correct message delivery in structured peer-to-peer overlays and presents defenses to these attacks. We describe and eval-uate techniques that allow nodes to join the overlay, to maintain routing state, and to forward messages securely in the presence of malicious nodes. 1

We characterize high-performance streaming applications as a new and distinct domain of programs that is becoming increasingly important.

Worm containment must be automatic because worms can spread too fast for humans to respond. Recent work proposed network-level techniques to automate worm containment; these techniques have limitations because there is no information about the vulnerabilities exploited by worms at the network level. We propose Vigilante, a new end-to-end architecture to contain worms automatically that addresses these limitations. In Vigilante, hosts detect worms by instrumenting vulnerable programs to analyze infection attempts. We introduce dynamic data-flow analysis: a broad-coverage host-based algorithm that can detect unknown worms by tracking the flow of data from network messages and disallowing unsafe uses of this data. We also show how to integrate other host-based detection mechanisms into the Vigilante architecture. Upon detection, hosts generate self-certifying alerts (SCAs), a new type of security alert that can be inexpensively verified by any vulnerable host. Using SCAs, hosts can cooperate to contain an outbreak, without having to trust each other. Vigilante broadcasts SCAs over an overlay network that propagates alerts rapidly and resiliently. Hosts receiving an SCA protect themselves by generating filters with vulnerability condition slicing: an algorithm that performs dynamic analysis of the vulnerable program to identify control-flow conditions that lead

Abstract—A large number of clustering approaches have been proposed for the analysis of gene expression data obtained from microarray experiments. However, the results from the application of standard clustering methods to genes are limited. This limitation is imposed by the existence of a number of experimental conditions where the activity of genes is uncorrelated. A similar limitation exists when clustering of conditions is performed. For this reason, a number of algorithms that perform simultaneous clustering on the row and column dimensions of the data matrix has been proposed. The goal is to find submatrices, that is, subgroups of genes and subgroups of conditions, where the genes exhibit highly correlated activities for every condition. In this paper, we refer to this class of algorithms as biclustering. Biclustering is also referred in the literature as coclustering and direct clustering, among others names, and has also been used in fields such as information retrieval and data mining. In this comprehensive survey, we analyze a large number of existing approaches to biclustering, and classify them in accordance with the type of biclusters they can find, the patterns of biclusters that are discovered, the methods used to perform the search, the approaches used to evaluate the solution, and the target applications. Index Terms—Biclustering, simultaneous clustering, coclustering, subspace clustering, bidimensional clustering, direct clustering, block clustering, two-way clustering, two-mode clustering, two-sided clustering, microarray data analysis, biological data analysis, gene expression data. 1

Group Diffie-Hellman protocols for Authenticated Key Exchange (AKE) are designed to provide a pool of players with a shared secret key which may later be used, for example, to achieve multicast message integrity. Over the years, several schemes have been offered. However, no formal treatment for this cryptographic problem has ever been suggested. In this paper, we present a security model for this problem and use it to precisely define AKE (with "implicit" authentication) as the fundamental goal, and the entity-authentication goal as well. We then define in this model the execution of an authenticated group Diffie-Hellman scheme and prove its security.

We present a new class of low-bandwidth denial of service attacks that exploit algorithmic deficiencies in many common applications' data structures. Frequently used data structures have "average-case" expected running time that's far more efficient than the worst case. For example, both binary trees and hash tables can degenerate to linked lists with carefully chosen input. We show how an attacker can effectively compute such input, and we demonstrate attacks against the hash table implementations in two versions of Perl, the Squid web proxy, and the Bro intrusion detection system. Using bandwidth less than a typical dialup modem, we can bring a dedicated Bro server to its knees; after six minutes of carefully chosen packets, our Bro server was dropping as much as 71% of its traffic and consuming all of its CPU. We show how modern universal hashing techniques can yield performance comparable to commonplace hash functions while being provably secure against these attacks.

Our program benchmarks and simulations of novel circuits indicate that large-window processors are feasible. Using our redesigned superscalar components, a large-window processor implemented in today’s technology can achieve an increase of 10–60 % (geometric mean of 31%) in program speed compared to today’s processors. The processor operates at clock speeds comparable to today’s processors, but achieves significantly higher ILP. To measure the impact of a large window on clock speed, we design and simulate new implementations of the logic components that most limit the critical path of our large-window processor: the schedule logic and the wake-up logic. We use log-depth cyclic segmented prefix (CSP) circuits to reimplement these components. Our layouts and simulations of critical paths through these circuits indicate that our large-window processor could be clocked at frequencies exceeding 500MHz in today’s technology. Our commit logic and rename logic can also run at these speeds. To measure the impact of a large window on ILP, we compare two microarchitectures, the first has a 128-instruction window, an 8-wide fetch unit, and 20-wide issue (four integer, branch, multiply, float, and memory units), whereas the second has a 32-instruction window, and a 4-wide fetch unit and is comparable to today’s processors. For each, we simulate different window reuse and bypass policies. Our simulations show that the large-window processor achieves significantly higher IPC. This performance increase comes despite the fact that the large-window processor uses a wrap-around window while the small-window processor uses a compressing window, thus effectively increasing its number of outstanding instructions. Furthermore, the large-window processor sometimes pays an extra clock cycle for bypassing. 1.

pSather is a parallel extension of the existing object-oriented language Sather. It offers a shared-memory programming model which integrates both control- and dataparallel extensions. This integration increases the flexibility of the language to express different algorithms and data structures, especially on distributed-memory machines (e.g. CM-5). This report describes our design objectives and the programming language pSather in detail. ICSI and Eidgenossische Technische Hochschule (ETH), Zurich, Switzerland. E-mail: murer@icsi.berkeley.edu. y ICSI and Computer Science Division, U.C. Berkeley. E-mail: jfeldman@icsi.berkeley.edu. z ICSI and Computer Science Division, U.C. Berkeley. E-mail: clim@icsi.berkeley.edu. x ICSI E-mail: mseidel@icsi.berkeley.edu. ii Contents 1 Introduction 4 1.1 Roadmap of this Report : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 5 1.2 Grammar Notation : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 6 2...

Formal concept analysis is increasingly used for large contexts that are built by programs. This paper presents an efficient algorithm for concept analysis that computes concepts together with their explicit lattice structure. An experimental evaluation uses randomly generated contexts to compare the running time of the presented algorithm with two other algorithms. Running time increases quadratically with the number of concepts, but with a small quadratic component. At least contexts with sparsely filled context tables cause concept lattices grow quadratically with respect to the size of their base relation. The growth rate is controlled by the density of context tables. Modest growth combined with efficient algorithms lead to fast concept analysis. 1

This paper explores nonparametric and semiparametric nonstationary modeling methodologies that couple stationary Gaussian processes and (limiting) linear models with treed partitioning. Partitioning is a simple but effective method for dealing with nonstationarity. Mixing between full Gaussian processes and simple linear models can yield a more parsimonious spatial model while significantly reducing computational effort. The methodological developments and statistical computing details which make this approach efficient are described in detail. Illustrations of our model are given for both synthetic and real datasets. Key words: recursive partitioning, nonstationary spatial model, nonparametric regression, Bayesian model averaging 1