Biblio

We propose a probabilistic approach to the problem of schema mapping. Our approach is declarative, scalable, and extensible. It builds upon recent results in both schema mapping and probabilistic reasoning and contributes novel techniques in both fields. We introduce the problem of mapping selection, that is, choosing the best mapping from a space of potential mappings, given both metadata constraints and a data example. As selection has to reason holistically about the inputs and the dependencies between the chosen mappings, we define a new schema mapping optimization problem which captures interactions between mappings. We then introduce Collective Mapping Discovery (CMD), our solution to this problem using stateof- the-art probabilistic reasoning techniques, which allows for inconsistencies and incompleteness. Using hundreds of realistic integration scenarios, we demonstrate that the accuracy of CMD is more than 33% above that of metadata-only approaches already for small data examples, and that CMD routinely finds perfect mappings even if a quarter of the data is inconsistent.

Expressing and matching the security policy of each participant accurately is the precondition to construct a secure service composition. Most schemes presently use syntactic approaches to represent and match the security policy for service composition process, which is prone to result in false negative because of lacking semantics. In this paper, a novel approach based on semantics is proposed to express and match the security policies in service composition. Through constructing a general security ontology, the definition method and matching algorithm of the semantic security policy for service composition are presented, and the matching problem of policy is translated into the subsumption reasoning problem of semantic concept. Both the theoretical analysis and experimental evaluation show that, the proposed approach can present the necessary semantic information in the representation of policy and effectively improve the accuracy of matching result, thus overcome the deficiency of the syntactic approaches, and can also simplify the definition and management of the policy at the same time, which thereby provides a more effective solution for building the secure service composition based on security policy.

Interface-confinement is a common mechanism that secures untrusted code by executing it inside a sandbox. The sandbox limits (confines) the code's interaction with key system resources to a restricted set of interfaces. This practice is seen in web browsers, hypervisors, and other security-critical systems. Motivated by these systems, we present a program logic, called System M, for modeling and proving safety properties of systems that execute adversary-supplied code via interface-confinement. In addition to using computation types to specify effects of computations, System M includes a novel invariant type to specify the properties of interface-confined code. The interpretation of invariant type includes terms whose effects satisfy an invariant. We construct a step-indexed model built over traces and prove the soundness of System M relative to the model. System M is the first program logic that allows proofs of safety for programs that execute adversary-supplied code without forcing the adversarial code to be available for deep static analysis. System M can be used to model and verify protocols as well as system designs. We demonstrate the reasoning principles of System M by verifying the state integrity property of the design of Memoir, a previously proposed trusted computing system.

Although current Internet operations generate voluminous data, they remain largely oblivious of traffic data semantics. This poses many inefficiencies and challenges due to emergent or anomalous behavior impacting the vast array of Internet elements such as services and protocols. In this paper, we propose a Data Semantics Management System (DSMS) for learning Internet traffic data semantics to enable smarter semantics- driven networking operations. We extract networking semantics and build and utilize a dynamic ontology of network concepts to better recognize and act upon emergent or abnormal behavior. Our DSMS utilizes: (1) Latent Dirichlet Allocation algorithm (LDA) for latent features extraction and semantics reasoning; (2) big tables as a cloud-like data storage technique to maintain large-scale data; and (3) Locality Sensitive Hashing algorithm (LSH) for reducing data dimensionality. Our preliminary evaluation using real Internet traffic shows the efficacy of DSMS for learning behavior of normal and abnormal traffic data and for accurately detecting anomalies at low cost.
 

The distinctive features of mobile ad hoc networks (MANETs), including dynamic topology and open wireless medium, may lead to MANETs suffering from many security vulnerabilities. In this paper, using recent advances in uncertain reasoning that originated from the artificial intelligence community, we propose a unified trust management scheme that enhances the security in MANETs. In the proposed trust management scheme, the trust model has two components: trust from direct observation and trust from indirect observation. With direct observation from an observer node, the trust value is derived using Bayesian inference, which is a type of uncertain reasoning when the full probability model can be defined. On the other hand, with indirect observation, which is also called secondhand information that is obtained from neighbor nodes of the observer node, the trust value is derived using the Dempster-Shafer theory (DST), which is another type of uncertain reasoning when the proposition of interest can be derived by an indirect method. By combining these two components in the trust model, we can obtain more accurate trust values of the observed nodes in MANETs. We then evaluate our scheme under the scenario of MANET routing. Extensive simulation results show the effectiveness of the proposed scheme. Specifically, throughput and packet delivery ratio (PDR) can be improved significantly with slightly increased average end-to-end delay and overhead of messages.

Multiple Security Domains Nondeducibility, MSDND, yields results even when the attack hides important information from electronic monitors and human operators. Because MSDND is based upon modal frames, it is able to analyze the event system as it progresses rather than relying on traces of the system. Not only does it provide results as the system evolves, MSDND can point out attacks designed to be missed in other security models. This work examines information flow disruption attacks such as Stuxnet and formally explains the role that implicit trust in the cyber security of a cyber physical system (CPS) plays in the success of the attack. The fact that the attack hides behind MSDND can be used to help secure the system by modifications to break MSDND and leave the attack nowhere to hide. Modal operators are defined to allow the manipulation of belief and trust states within the model. We show how the attack hides and uses the operator's trust to remain undetected. In fact, trust in the CPS is key to the success of the attack.

This study describes and evaluates a novel trust model for a range of collaborative applications. The model assumes that humans routinely choose to trust their peers by relying on few recurrent presumptions, which are domain independent and which form a recognisable trust expertise. We refer to these presumptions as trust schemes, a specialised version of Walton's argumentation schemes. Evidence is provided about the efficacy of trust schemes using a detailed experiment on an online community of 80,000 members. Results show how proposed trust schemes are more effective in trust computation when they are combined together and when their plausibility in the selected context is considered.