Malicious URLs have become a channel for Internet criminal activities such as drive-by-download, spamming and phishing. Applications for the detection of malicious URLs are accurate but slow (because they need to download the content or query some Internet host information). In this paper we present a novel lightweight filter based only on the URL string itself to use before existing processing methods. We run experiments on a large dataset and demonstrate a 75% reduction in workload size while retaining at least 90% of malicious URLs. Existing methods do not scale well with the hundreds of millions of URLs encountered every day as the problem is a heavily-imbalanced, large-scale binary classification problem. Our proposed method is able to handle nearly two million URLs in less than five minutes. We generate two filtering models by using lexical features and descriptive features, and then combine the filtering results. The on-line learning algorithms are applied here not only for dealing with large-scale data sets but also for fitting the very short lifetime characteristics of malicious URLs. Our filter can significantly reduce the volume of URL queries on which further analysis needs to be performed, saving both computing time and bandwidth used for content retrieval.

This study presents an O(k/sup 2//spl middot/log(n)) algorithm for computing the reliability of a linear as well as a circular consecutive-k-out-of-n: F system. The proposed algorithm is more efficient and much simpler than the O(k/sup 3//spl middot/log(n/k)) algorithm of Hwang & Wright.

Consider a probabilistic graph in which the edges are perfectly reliable, but vertices can fail with some known probabilities. The K-terminal reliability of this graph is the probability that a given set of vertices K is connected. This reliability problem is #P-complete for general graphs, and remains #P-complete for chordal graphs and comparability graphs. This paper presents a linear-time algorithm for computing K-terminal reliability on proper interval graphs. A graph G = (V, E) is a proper interval graph if there exists a mapping from V to a class of intervals I of the real line with the properties that two vertices in G are adjacent if their corresponding intervals overlap and no interval in I properly contains another. This algorithm can be implemented in O(|V| + |E|) time.

The reliability of a distributed computing system depends on the reliability of its communication links and nodes and on the distribution of its resources, such as programs and data files. Many algorithms have been proposed for computing the reliability of distributed computing systems, but they have been applied mostly to distributed computing systems with perfect nodes. However, in real problems, nodes as well as links may fail. This paper proposes two new algorithms for computing the reliability of a distributed computing system with imperfect nodes. Algorithm I is based on a symbolic approach that includes two passes of computation. Algorithm II employs a general factoring technique on both nodes and edges. Comparisons with existing methods show the usefulness of the proposed algorithms for computing the reliability of large distributed computing systems.

We show that computing distributed program reliability on the star distributed computing system is NP-hard. We develop a polynomially solvable case to compute distributed program reliability when some additional file distribution is restricted on the star topology. We also propose a polynomial time algorithm for computing distributed program reliability with an approximate solution when the star topology is not satisfied with the additional file distribution.

Presents an algorithm for computing the reliability of distributed computing systems (DCS). The algorithm, called the Fast Reliability Evaluation Algorithm, is based on the factoring theorem employing several reliability preserving reduction techniques. The effect of file distributions, program distributions, and various topologies on reliability of the DCS is studied in detail using the proposed algorithm. Compared with existing algorithms on various network topologies, file distributions, and program distributions, the proposed algorithm is much more economical in both time and space. To compute the distributed program reliability, the ARPA network is studied to illustrate the feasibility of the proposed algorithm.>

The reliability of a distributed computing system is the probability that a distributed program which runs on multiple processing elements and needs to communicate with other processing elements for remote data files will be executed successfully. This reliability varies according to (1) the topology of the distributed computing system, (2) the reliability of the communication links, (3) the data files and program distribution among processing elements, and (4) the data files required to execute a program. Thus, the problem of analyzing the reliability of a distributed computing system is more complicated than the K-terminal reliability problem, and many of the reliability-preserving reductions for speeding up the computation of the K-terminal reliability cannot be applied to this problem. In this paper, we shall propose several reduction methods for computing the reliability of distributed computing systems. These reduction methods can dramatically reduce the size of a distributed computing systems, and therefore speed up the reliability computation.