15067408
Adapting LDA Model to Discover Author-Topic Relations for Email Analysis
Analyzing the author and topic relations in email corpus is an important issue in both social network analysis and text mining. The Author-Topic model is a statistical model that identifies the author-topic relations. However, in its inference process, it ignores the information at the document level, i.e., the co-occurrence of words within documents are not taken into account in deriving topics. This may not be suitable for email analysis. We propose to adapt the Latent Dirichlet Allocation model for analyzing email corpus. This method takes into account both the author-document relations and the document-topic relations. We use the Author-Topic model as the baseline method and propose measures to compare our method against the Author-Topic model. We did empirical analysis based on experimental results on both simulated data sets and the real Enron email data set to show that our method obtains better performance than the Author-Topic model.
36787117
Parallelizing Support Vector Machines on Distributed Computers
Support Vector Machines (SVMs) suffer from a widely recognized scalability problem in both memory use and computational time. To improve scalability, we have developed a parallel SVM algorithm (PSVM), which reduces memory use through performing a row-based, approximate matrix factorization, and which loads only essential data to each machine to perform parallel computation. Let n denote the number of training instances, p the reduced matrix dimension after factorization (p is significantly smaller than n), and m the number of machines. PSVM reduces the memory requirement from O(n2) to O(np/m), and improves computation time to O(np2/m). Empirical study shows PSVM to be effective. PSVM Open Source is available for download at http://code.google.com/p/psvm/.
7757393
Reliability as an interdomain service
Reliability is a critical requirement of the Internet. The availability and resilience of the Internet under failures can have significant global effects. However, in the current Internet routing architecture, achieving the high level of reliability demanded by many mission critical activities can be costly. In this paper, we first propose a novel solution framework called reliability as an interdomain service (REIN) that can be incrementally deployed in the Internet and may improve the redundancy of IP networks at low cost. We then present robust algorithms to efficiently utilize network redundancy to improve reliability. We use real IP network topologies and traffic traces to demonstrate the effectiveness of our framework and algorithms.
10887794
Packet doppler: network monitoring using packet shift detection
Due to recent large-scale deployments of delay and loss-sensitive applications, there are increasingly stringent demands on the monitoring of service level agreement metrics. Although many end-to-end monitoring methods have been proposed, they are mainly based on active probing and thus inject measurement traffic into the network. In this paper, we propose a new scheme for monitoring service level agreement metrics, in particular, delay distribution. Our scheme is passive and therefore will not cause perturbation to real traffic. Using realistic delay and traffic demands, we show that our scheme achieves high accuracy and can detect burst events that will be missed by probing based methods.
1344510
COPE: traffic engineering in dynamic networks
Traffic engineering plays a critical role in determining the performance and reliability of a network. A major challenge in traffic engineering is how to cope with dynamic and unpredictable changes in traffic demand. In this paper, we propose COPE, a class of traffic engineering algorithms that optimize for the expected scenarios while providing a worst-case guarantee for unexpected scenarios. Using extensive evaluations based on real topologies and traffic traces, we show that COPE can achieve efficient resource utilization and avoid network congestion in a wide variety of scenarios.
4379994
Interdomain Routing as Social Choice
Interdomain routing is essential to both the stability and efficiency of the global Internet. However, most previous studies focus only on stability, and only on a special class of routing protocols, namely BGP-type, path-vector protocols. In this paper, we conduct a systematic analysis of interdomain routing considering optimality and implementation in strategic settings. We adopt the novel perspective that an interdomain routing system is one which defines a social choice rule that aggregates individual preferences of all of the autonomous systems (ASes) in a network to select interdomain routes with a set of desirable properties. An interdomain routing protocol, then, is a mechanism to implement the identified interdomain routing social choice rule, when the ASes can adopt strategic actions. By pointing out the incompatibility among the desirable properties of an interdomain routing system and the requirements for strategic implementation in distributed settings, we reveal fundamental tradeoffs that must be made when extending BGP or designing the next-generation interdomain routing system. We also provide new insights into BGP, by "reverseengineering" its behaviors from the perspective of social choice and implementation theory.
8422890
Spatiotemporal Phase Congruency Based Invariant Features for Human Behavior Classification
In applications of behavior recognition, the use of spatiotemporal invariant feature points can improve the robustness to noise, illumination and geometric distortions. In this paper, we develop a novel detection model of spatiotemporal invariant feature by generalizing the notion of image phase congruency to video volume phase congruency. The proposed model detects feature points by measuring the spatiotemporal phase congruency of Fourier series components along with their characteristic scale and principal orientation. Compared with other state-of-the-art methods, the key advantages of this interest point detector include the invariance to contrast variations and more precise feature location. Furthermore, an invariant feature descriptor is advanced based on the phase congruency map, resulting in enhanced discriminative power in classification tasks. Experimental results on KTH human motion dataset demonstrate the validity and effectiveness of the extracted invariant features in the human behavior recognition scheme.
45158244
Discovering pattern-based subspace clusters by pattern tree
Traditional clustering models based on distance similarity are not always effective in capturing correlation among data objects, while pattern-based clustering can do well in identifying correlation hidden among data objects. However, the state-of-the-art pattern-based clustering methods are inefficient and provide no metric to measure the clustering quality. This paper presents a new pattern-based subspace clustering method, which can tackle the problems mentioned above. Observing the analogy between mining frequent itemsets and discovering subspace clusters, we apply pattern tree - a structure used in frequent itemsets mining to determining the target subspaces by scanning the database once, which can be done efficiently in large datasets. Furthermore, we introduce a general clustering quality evaluation model to guide the identifying of meaningful clusters. The proposed new method enables the users to set flexibly proper quality-control parameters to meet different needs. Experimental results on synthetic and real datasets show that our method outperforms the existing methods in both efficiency and effectiveness.
16457860
A New Secure Authentication Scheme Based Threshold ECDSA For Wireless Sensor Network
Security is an important issue for Wireless Sensor Network (WSN), especially for security-sensitive application. In this paper, we address secure authentication problems in WSN. First, we propose distributed authentication model for WSN, and secondly, advance the threshold ECDSA authentication scheme in this model, finally construct a new way to choose distributed CA severs of threshold authentication scheme. Our simulation results and analysis show that our scheme can reduce effectively bandwidth and computation resources while providing a robust, highly secure and highly available authentication service.
11355624
Theory and Techniques for Automatic Generation of Vulnerability-Based Signatures
In this paper, we explore the problem of creating \emph{vulnerability signatures}. A vulnerability signature is based on a program vulnerability, and is not specific to any particular exploit. The advantage of vulnerability signatures is that their quality can be guaranteed. In particular, we create vulnerability signatures which are guaranteed to have zero false positives. We show how to automate signature creation for any vulnerability that can be detected by a runtime monitor. We provide a formal definition of a vulnerability signature, and investigate the computational complexity of creating and matching vulnerability signatures. We systematically explore the design space of vulnerability signatures. We also provide specific techniques for creating vulnerability signatures in a variety of language classes. In order to demonstrate our techniques, we have built a prototype system. Our experiments show that we can, using a single exploit, automatically generate a vulnerability signature as a regular expression, as a small program, or as a system of constraints. We demonstrate techniques for creating signatures of vulnerabilities which can be exploited via multiple program paths. Our results indicate that our approach is a viable option for signature generation, especially when guarantees are desired.
40601169
Performance Modeling and Practical Parallel Algorithms of Cluster Computing with Applications on Distributed Platforms