Proof of Service in a Hybrid P2P Environment
In a hybrid peer-to-peer environment, clients can either directly download data from their server, or share data with each other. In order to create incentives for clients to share data and decrease server load, an effective economy model is for the server to credit those provider clients who provide data to others and offer discounts to those recipient clients who download data from provider clients instead of the server. To realize this model, the proof of service between provider and recipient clients must be provided. 
 
We design and investigate three different schemes and compare them in terms of scalability, effectiveness, and cost. We emphasize the issues of lessening the number of proofs which must be provided, avoiding a heavy load on the server, and ensuring the proof for every piece of data served. Our study shows our enhanced public-key-based scheme to be the most effective.

title: mSSL: extending SSL to support data sharing among collaborative clients
abstract: Client-server applications often do not scale well when a large number of clients access a single server. To solve this, a new trend is to allow a client to download data from other peer clients, in addition to from the server directly. This paradigm, which we call the hybrid peer-to-peer paradigm, is friendly to the server's scalability, but also faces new security challenges. For example, how can the server authenticate its clients and support data confidentiality? How can a client trust the data downloaded from other clients? What if a client refuses to acknowledge the service it received or overstates the service it offered? In this paper, we present a protocol, called mSSL, that provides a set of security functions to enable secure sharing of the data of a server among its clients. In addition to access control and confidentiality support, mSSL provides an original design on supporting data integrity and proof of service in this new context. Our evaluation further shows that mSSL has a reasonable overhead

title: Securing distributed adaptation
abstract: Open architecture networks provide applications with fine-grained control over network elements. With this control comes the risk of misuse and new challenges to security beyond those present in conventional networks. One particular security requirement is the ability of applications to protect the secrecy and integrity of transmitted data while still allowing trusted active elements within the network to operate on that data. This paper describes mechanisms for identifying trusted nodes within a network and securely deploying adaptation instructions to those nodes while protecting application data from unauthorized access and modification. Promising experimental results of our implementation within the conductor adaptation framework are also presented, suggesting that such features can be incorporated into real networks.

title: SAVE: source address validity enforcement protocol
abstract: Forcing all IP packets to carry correct source addresses can greatly help network security, attack tracing, and network problem debugging. However, due to asymmetries in today's Internet routing, routers do not have readily available information to verify the correctness of the source address for each incoming packet. In this paper we describe a new protocol, named SAVE, that can provide routers with the information needed for source address validation. SAVE messages propagate valid source address information from the source location to all destinations, allowing each router along the way to build an incoming table that associates each incoming interface of the router with a set of valid source address blocks. This paper presents the protocol design and evaluates its correctness and performance by simulation experiments. The paper also discusses the issues of protocol security, the effectiveness of partial SAVE deployment, and the handling of unconventional forms of network routing, such as mobile IP and tunneling.

title: An internet routing forensics framework for discovering rules of abnormal BGP events
abstract: Abnormal BGP events such as attacks, misconfigurations, electricity failures, can cause anomalous or pathological routing behavior at either global level or prefix level, and thus must be detected in their early stages. Instead of using ad hoc methods to analyze BGP data, in this paper we introduce an Internet Routing Forensics framework to systematically process BGP routing data, discover rules of abnormal BGP events, and apply these rules to detect the occurrences of these events. In particular, we leverage data mining techniques to train the framework to learn rules of abnormal BGP events, and our results from two case studies show that these rules are effective. In one case study, rules of worm events discovered from the BGP data during the outbreaks of the CodeRed and Nimda worms were able to successfully detect worm impact on BGP when an independent worm, the Slammer, subsequently occurred. Similarly, in another case study, rules of electricity blackout events obtained using BGP data from the 2003 East Coast blackout were able to detect the BGP impact from the Florida blackout caused by Hurricane Frances in 2004.

title: BGP routing dynamics revisited
abstract: Understanding BGP routing dynamics is critical to the solid growth and maintenance of the Internet routing infrastructure. However, while the most extensive study on BGP dynamics is nearly a decade old, many factors that could affect BGP dynamics have changed considerably. We revisit this important topic in this paper, focusing on not only comparing with the previous results, but also issues not well explored before. We have found that, compared to almost a decade ago, although certain characteristics remain unchanged (such as some temporal properties), BGP dynamics are now â busier,â and more importantly, now have much less pathological behavior and are â healthierâ ; for example, forwarding dynamics are now not only dominant, but also more consistent across different days. Contributions to BGP dynamics by different BGP peersâ which are not proportional to the size of a peerâ s ASâ are also more stable, and dynamics due to policy changes or duplicate announcements are usually from specific peers

title: Relative risk and odds ratio: a data mining perspective
abstract: We are often interested to test whether a given cause has a given effect. If we cannot specify the nature of the factors involved, such tests are called model-free studies. There are two major strategies to demonstrate associations between risk factors (ie. patterns) and outcome phenotypes (ie. class labels). The first is that of prospective study designs, and the analysis is based on the concept of "relative risk": What fraction of the exposed (ie. has the pattern) or unexposed (ie. lacks the pattern) individuals have the phenotype (ie. the class label)? The second is that of retrospective designs, and the analysis is based on the concept of "odds ratio": The odds that a case has been exposed to a risk factor is compared to the odds for a case that has not been exposed. The efficient extraction of patterns that have good relative risk and/or odds ratio has not been previously studied in the data mining context. In this paper, we investigate such patterns. We show that this pattern space can be systematically stratified into plateaus of convex spaces based on their support levels. Exploiting convexity, we formulate a number of sound and complete algorithms to extract the most general and the most specific of such patterns at each support level. We compare these algorithms. We further demonstrate that the most efficient among these algorithms is able to mine these sophisticated patterns at a speed comparable to that of mining frequent closed patterns, which are patterns that satisfy considerably simpler conditions.

title: RACCOON: a peer-based system for data integration and sharing
abstract: Recent database applications see the emerging need to support data integration in distributed, peer-to-peer environments, in which autonomous peers (sources) connected by a network are willing to exchange data and services with each other. To address related research challenges, we are developing a system called "RACCOON", which allows different sources to integrate and share their data. We use an application to show several important features of the RACCOON system. The system also suggests semantic mappings for the user to choose. We show the two different querying modes, particularly how a query is expanded using the semantic mappings in the extended querying mode to compute as many answers to the query as possible.

title: Introducing a Rule Importance Measure
abstract: Association rule algorithms often generate an excessive number of rules, many of which are not significant. It is difficult to determine which rules are more useful, interesting and important. We introduce a rough set based Rule Importance Measure to select the most important rules. We use ROSETTA software to generate multiple reducts. Apriori association rule algorithm is then applied to generate rule sets for each data set based on each reduct. Some rules are generated more frequently than the others among the total rule sets. We consider such rules as more important. We define rule importance as the frequency of an association rule generated across all the rule sets. Rule importance is different from either rule interestingness measures or rule quality measures because of their application tasks, the processes where the measures are applied and the contents they measure. The experimental results from an artificial data set, UCI machine learning datasets and an actual geriatric care medical data set show that our method reduces the computational cost for rule generation and provides an effective measure of how important is a rule.

title: Cycles through subsets with large degree sums
abstract: Let G be a 2-connected graph on n vertices and let X V(G). We say that G is X-cyclable if G has an X-cycle, i.e., a cycle containing all vertices of X. We denote by ?(X) the maximum number of pairwise nonadjacent vertices in the subgraph G[X] of G induced by X. If G[X] is not complete, we denote by ?(X) the minimum cardinality of a set of vertices of G separating two vertices of X. By ?(X) we denote the minimum degree (in G) of the vertices of X, and by ?) the minimum value of the degree sum (in G) of any three pairwise nonadjacent vertices of X. Our first main result is the following extension in terms of X-cyclability of a result on hamiltonian graphs by Bauer et al. If ?) n + mingk(X), ?(X), then G is X-cyclable. Our second main result is the following generalization of a result of Fournier. If ?(X) ?(X), then G is X-cyclable. We give a number of extensions of other known results, thereby generalizing some recent results of Veldman.

title: Parameter estimation of multi-dimensional hidden Markov models - a scalable approach
abstract: Parameter estimation is a key computational issue in all statistical image modeling techniques. In this paper, we explore a computationally efficient parameter estimation algorithm for multi-dimensional hidden Markov models. 2-D HMM has been applied to supervised aerial image classification and comparisons have been made with the first proposed estimation algorithm. An extensive parametric study has been performed with 3-D HMM and the scalability of the estimation algorithm has been discussed. Results show the great applicability of the explored algorithm to multi-dimensional HMM based image modeling applications.