72839
Distributing streaming media content using cooperative networking
In this paper, we discuss the problem of distributing streaming media content, both live and on-demand, to a large number of hosts in a scalable way. Our work is set in the context of the traditional client-server framework. Specifically, we consider the problem that arises when the server is overwhelmed by the volume of requests from its clients. As a solution, we propose Cooperative Networking (CoopNet), where clients cooperate to distribute content, thereby alleviating the load on the server. We discuss the proposed solution in some detail, pointing out the interesting research issues that arise, and present a preliminary evaluation using traces gathered at a busy news site during the flash crowd that occurred on September 11, 2001.
17331850
A Systematic Approach to Uncover Security Flaws in GUI Logic
To achieve end-to-end security, traditional machine-to-machine security measures are insufficient if the integrity of the human-computer interface is compromised. GUI logic flaws are a category of software vulnerabilities that result from logic bugs in GUI design/implementation. Visual spoofing attacks that exploit these flaws can lure even security- conscious users to perform unintended actions. The focus of this paper is to formulate the problem of GUI logic flaws and to develop a methodology for uncovering them in software implementations. Specifically, based on an in-depth study of key subsets of Internet Explorer (IE) browser source code, we have developed a formal model for the browser GUI logic and have applied formal reasoning to uncover new spoofing scenarios, including nine for status bar spoofing and four for address bar spoofing. The IE development team has confirmed all these scenarios and has fixed most of them in their latest build. Through this work, we demonstrate that a crucial subset of visual spoofing vulnerabilities originate from GUI logic flaws, which have a well-defined mathematical meaning allowing a systematic analysis.
13895152
ShieldGen: Automatic Data Patch Generation for Unknown Vulnerabilities with Informed Probing
In this paper, we present ShieldGen, a system for automatically generating a data patch or a vulnerability signature for an unknown vulnerability, given a zero-day attack instance. The key novelty in our work is that we leverage knowledge of the data format to generate new potential attack instances, which we call probes, and use a zero-day detector as an oracle to determine if an instance can still exploit the vulnerability; the feedback of the oracle guides our search for the vulnerability signature. We have implemented a ShieldGen prototype and experimented with three known vulnerabilities. The generated signatures have no false positives and a low rate of false negatives due to imperfect data format specifications and the sampling technique used in our probe generation. Overall, they are significantly more precise than the signatures generated by existing schemes. We have also conducted a detailed study of 25 vulnerabilities for which Microsoft has issued security bulletins between 2003 and 2006. We estimate that ShieldGen can produce high quality signatures for a large portion of those vulnerabilities and that the signatures are superior to the signatures generated by existing schemes.
9905659
A signaling system using lightweight call sessions
Because of the emergence of heterogeneous access devices and diverse wired and wireless networks, and a substantial lack of support for integrating these networks, we are building a communication network and a service infrastructure that provides integrated telephony and data services across these networks. We generalize the basic call service to support communication between two or more call parties using any number of devices through any media. Call setup has already been addressed by protocols such as the session initiation protocol (SIP), so we focus on a missing component: scalable and fault-tolerant call session maintenance and control after call setup. In this paper, we present a new signaling protocol that offers scalability, high availability, robustness, and flexibility in creating new call processing services. The protocol is compatible with existing call setup protocols, but provides lightweight call session management using a completely decentralized, soft-state group control protocol. We show that this approach simplifies the implementation of the basic call service, including multi-device calling and service handoff between diverse access networks. The design of our protocol follows the principle of separation of control (signaling information) from data. The signaling protocol has been implemented in ICEBERG, an IP-based core network testbed with access to heterogeneous networks.
42916380
Policy-enabled handoffs across heterogeneous wireless networks
"Access is the killer app" is the vision of the Daedalus project at UC Berkeley. Being able to be connected seamlessly anytime anywhere to the best network still remains an unfulfilled goal. Often, even determining the "best" network is a challenging task because of the widespread deployment of overlapping wireless networks. We describe a policy-enabled handoff system that allows users to express policies on what is the "best" wireless system at any moment, and make tradeoffs among network characteristics and dynamics such as cost, performance and power consumption. We designed a performance reporting scheme estimating current network conditions, which serves as input to the policy specification. A primary goal of this work is to make it possible to balance the bandwidth load across networks with comparable performance. To avoid the problem of handoff instability, i.e., many mobile hosts making the same handoff decision at essentially the same time, we designed randomization into our mechanism. Given the current "best" network, our system determines whether the handoff is worthwhile based on the handoff overhead and potential network usage duration.
1513566
Passive network tomography using Bayesian inference
In this paper, we investigate the problem of identifying lossy links in the interior of the Internet by passively observing the end-to-end performance of existing traffic between a server and its clients. This is in contrast to the previous work on network tomography (e.g., [1]) that has been based on active probing. The key advantage of a passive approach is that it does not introduce wasteful traffic which might perturb the object of inference, i.e., the link loss rates. Moreover, our techniques depend only on knowing the number of lost and successful packets sent to each client rather than the exact loss sequence required by previous techniques such as [1]. While accuracy of link loss rate inference may consequently suffer, our techniques can still pinpoint the trouble spots in the network (e.g., highly lossy links). We have developed three techniques for passive network tomography: Random Sampling, Linear Optimization, and Bayesian Inference using Gibbs Sampling. We have evaluated these techniques using simulations and traces gathered at a busy Web server. In this paper, we focus on the Gibbs Sampling technique; more information on the three techniques appears in [3].
2366018
Constrained texture synthesis by scalable sub-patch algorithm
In this paper, we propose a novel scalable sub-patch algorithm for texture synthesis. Texture synthesis is an important research topic in computer graphics and image processing. We present an efficient algorithm which can paste a patch each time without boundary processing technique. In particular, the proposed sub-patch algorithm can be used in constrained texture synthesis to fill the large lost region. On the other hand, the searching neighborhood pixels can be changed due to different patch size in image reconstruction. The comparison of previous algorithms is also provided in this paper. We present a first algorithm used in constrained texture synthesis by regular synthesizing order
10547222
A Hybrid Ontology Directed Feedback Selection Algorithm for Supporting Creative Problem Solving Dialogues
We evaluate a new hybrid language processing approach designed for interactive applications that maintain an interaction with users over multiple turns. Specifically, we describe a method for using a simple topic hierarchy in combination with a standard information retrieval measure of semantic similarity to reason about the selection of appropriate feedback in response to extended language inputs in the context of an interactive tutorial system designed to support creative problem solving. Our evaluation demonstrates the value of using a machine learning approach that takes feedback from experts into account for optimizing the hierarchy based feedback selection strategy.
2866284
BLINKS: ranked keyword searches on graphs
Query processing over graph-structured data is enjoying a growing number of applications. A top-k keyword search query on a graph finds the top k answers according to some ranking criteria, where each answer is a substructure of the graph containing all query keywords. Current techniques for supporting such queries on general graphs suffer from several drawbacks, e.g., poor worst-case performance, not taking full advantage of indexes, and high memory requirements. To address these problems, we propose BLINKS, a bi-level indexing and query processing scheme for top-k keyword search on graphs. BLINKS follows a search strategy with provable performance bounds, while additionally exploiting a bi-level index for pruning and accelerating the search. To reduce the index space, BLINKS partitions a data graph into blocks: The bi-level index stores summary information at the block level to initiate and guide search among blocks, and more detailed information for each block to accelerate search within blocks. Our experiments show that BLINKS offers orders-of-magnitude performance improvement over existing approaches.
34944417
Logic of Many-Sorted Theories
Certain axiomatic systems involve more than one category of fundamental objects; for example, points, lines, and planes in geometry; individuals, classes of individuals, etc. in the theory of types or in predicate calculi of orders higher than one. It is natural to use variables of different kinds with their ranges respectively restricted to different categories of objects, and to assume as substructure the usual quantification theory (the restricted predicate calculus) for each of the various kinds of variables together with the usual theory of truth functions for the formulas of the system. An axiomatic theory set up in this manner will be called many-sorted. We shall refer to the theory of truth functions and quantifiers in it as its (many-sorted) elementary logic, and call the primitive symbols and axioms (including axiom schemata) the proper primitive symbols and proper axioms of the system. Our purpose in this paper is to investigate the many-sorted systems and their elementary logics. Among the proper primitive symbols of a many-sorted system T n ( n = 2, …, ω ) there may be included symbols of some or all of the following kinds: (1) predicates denoting the properties and relations treated in the system; (2) functors denoting the functions treated in the system; (3) constant names for certain objects of the system. We may either take as primitive or define a predicate denoting the identity relation in T n .
34060618
A Minimum Boundary Error Framework for Automatic Phonetic Segmentation
This paper presents a novel framework for HMM-based automatic phonetic segmentation that improves the accuracy of placing phone boundaries. In the framework, both training and segmentation approaches are proposed according to the minimum boundary error (MBE) criterion, which tries to minimize the expected boundary errors over a set of possible phonetic alignments. This framework is inspired by the recently proposed minimum phone error (MPE) training approach and the minimum Bayes risk decoding algorithm for automatic speech recognition. To evaluate the proposed MBE framework, we conduct automatic phonetic segmentation experiments on the TIMIT acoustic-phonetic continuous speech corpus. MBE segmentation with MBE-trained models can identify 80.53% of human-labeled phone boundaries within a tolerance of 10 ms, compared to 71.10% identified by conventional ML segmentation with ML-trained models. Moreover, by using the MBE framework, only 7.15% of automatically labeled phone boundaries have errors larger than 20 ms.