2868417
Privacy-Preserving Friends Troubleshooting Network
Content sharing is a popular use of peer-to-peer systems because of their inherent scalability and low cost of maintenance. In this paper, we leverage this nature of peer-topeer systems to tackle a new problem: automatic misconfiguration troubleshooting. In this setting, machine configurations from peers are shared to diagnose misconfigurations on a sick machine. The key challenges are preserving privacy of individual configuration data and ensuring the integrity of peer contributions. To this end, we construct the Friends Troubleshooting Network (FTN), a peer-to-peer overlay network, where the links between peer machines reflect the friendship of their owners. Our FTN manifests recursive trust rather than transitive trust. To achieve privacy, we use the general scheme of a historyless and futureless random-walk for routing, during which search is carried out simultaneously with secure parameter aggregation for the purpose of troubleshooting. Our design has been guided by the characteristics of a real-world friends network, the MSN Instant Messenger (IM) network. We have prototyped our FTN system and analyzed the tradeoff between privacy and protocol efficiency.
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.
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.
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.
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].
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.
15118493
Anomaly intrusion detection using multi-objective genetic fuzzy system and agent-based evolutionary computation framework
In this paper, we present a multi-objective genetic fuzzy system for anomaly intrusion detection. The proposed system extracts accurate and interpret able fuzzy rule-based knowledge from network data using an agent-based evolutionary computation framework. The experimental results on KDD-Cup99 intrusion detection benchmark data demonstrate that our system can achieve high detection rate for intrusion attacks and low false positive rate for normal network traffic.
14931170
Towards Automatic Identification Of Singing Language In Popular Music Recordings
The automatic analysis of singing from music is an important and challenging issue within the research target of content-based retrieval of music information. As part of this research target, this study presents a first attempt to automatically identify the language sung in a music recording. It is assumed that each language has its own set of constraints that specify which of the basic linguistic events present in a singing process are allowed to follow another. The acoustic structure of individual languages may, thus, be characterized by statistically modeling those constraints. To this end, the proposed method employs vector clustering to convert a singing signal from its spectrum-based feature representation into a sequence of smaller basic phonological units. The dynamic characteristics of the sequence are then analyzed by using bigram language models. Since the vector clustering is performed in an unsupervised manner, the resulting system does not use sophisticated linguistic knowledge and, thus, is easily portable to new language sets. In addition, to eliminate the interference of background music, we leverage the statistical estimation of a piece’s music background so that the vector clustering is relevant to the solo singing voices in the accompanied signals.
44405113
Data hiding techniques for printed binary images
The objective of this research is to develop a method to hide information inside a binary image by digital halftoning techniques with certain modifications. Two modified digital halftoning techniques, modified ordered dithering and modified multiscale error diffusion, are used in this research. The data is encoded pixel by pixel in the halftone image according to position at the image and sequence of binarization, respectively. The eye model and mean square error are used to measure the image quality. A computer vision method has been developed to recognize the printed binary image. The results show that thousands of binary images similar to human vision but quite distinct from each other by computer vision can be generated. The eye model and computer vision are useful for both binary image quality measurement and data recognition. These new techniques have great potential in printing security documents such as currency, ID card as well as confidential documents.
376739
An empirical exploration of using Wiki in an English as a second language course
In this paper, we present an empirical study of using a new and cost-effective Web-based collaboration software, Wiki, in a freshman-level English as a second language (ESL) course. This paper explores and observes the scenario: what if the Wiki tool were to be used in an English as a second language course in Taiwan? Students who attended this study practiced English writing on a Wiki Web site. The data about their usage and learning achievements was collected and analyzed. Our finding of a significant, but inverse, relation between students' editing usage and academic performance challenges some idealistic hypotheses that Wiki technology is "naturally beneficial" to learning. We believe that building an instructive or constructive instructional model with Wiki in a rigorous manner requires more empirical evidence. This study provides fresh evidence that will hopefully serve as an impetus to fill that gap.
15210668
A Music Retrieval System Based on Query-by-Singing for Karaoke Jukebox
This paper investigates the problem of retrieving Karaoke music by singing. The Karaoke music encompasses two audio channels in each track: one is a mix of vocal and background accompaniment, and the other is composed of accompaniment only. The accompaniments in the two channels often resemble each other, but are not identical. This characteristic is exploited to infer the vocal’s background music from the accompaniment-only channel, so that the main melody underlying the vocal signals can be extracted more effectively. To enable an efficient and accurate search for a large music database, we propose a phrase onset detection method based on Bayesian Information Criterion (BIC) for predicting the most likely beginning of a sung query, and adopt a multiple-level multiple-pass Dynamic Time Warping (DTW) for melody similarity comparison. The experiments conducted on a Karaoke database consisting of 1,071 popular songs show the promising results of query-by-singing retrieval for Karaoke music.