A Protocol for Authentication with Multiple Levels of Anonymity (AMLA) in VANETs

The basic requirements for secure communication in a vehicular ad hoc network (VANET) are anonymous authentication with source non-repudiation and integrity. The existing security protocols in VANETs do not differentiate between the anonymity requirements of different vehicles and the level of anonymity provided by these protocols is the same for all the vehicles in a network. To provide high level of anonymity, the resource requirements of security protocol would also be high. Hence, in a resource constrained VANET, it is necessary to differentiate between the anonymity requirements of different vehicles and to provide the level of anonymity to a vehicle as per its requirement. In this paper, we have proposed a novel protocol for authentication which can provide multiple levels of anonymity in VANETs. The protocol makes use of identity based signature mechanism and pseudonyms to implement anonymous authentication with source non-repudiation and integrity. By controlling the number of pseudonyms issued to a vehicle and the lifetime of each pseudonym for a vehicle, the protocol is able to control the level of anonymity provided to a vehicle. In addition, the protocol includes a novel pseudonym issuance policy using which the protocol can ensure the uniqueness of a newly generated pseudonym by checking only a very small subset of the set of pseudonyms previously issued to all the vehicles. The protocol cryptographically binds an expiry date to each pseudonym, and in this way, enforces an implicit revocation for the pseudonyms. Analytical and simulation results confirm the effectiveness of the proposed protocol.

基于属性的访问控制模型

利用受限数据库为理论对访问请求、属性权威、策略和判定过程的抽象描述，给出了一个基于属性的访问控制模型，讨论了模型中访问请求、属性权威、策略和判定过程之间的关系，给出了一个访问控制判定过程可终止的一种特定条件。

一种面向基于属性存取的文件系统的设计与实现

基于属性的存取，对于在包含不同来源的大量文件的系统中进行有效的信息管理来说，是一种非常具有吸引力的特性。然而尽管在相当长的一段时间内其价值已广为人知，该特性的真正有实用价值的实现仍然非常之少。本文探讨了实现该特性时面临的主要挑战，以及它们是怎样在一种专为此目的设计的文件系统中，通过成功的应用各种原则而被妥善处理。

Identification and validation of the dopamine agonist bromocriptine as a novel therapy for high-risk myelodysplastic syndromes and secondary acute myeloid leukemia

Myelodysplastic syndromes (MDS) represent a broad spectrum of diseases characterized by their clinical manifestation as one or more cytopenias, or a reduction in circulating blood cells. MDS is predominantly a disease of the elderly, with a median age in the UK of around 75. Approximately one third of MDS patients will develop secondary acute myeloid leukemia (sAML) that has a very poor prognosis. Unfortunately, most standard cytotoxic agents are often too toxic for older patients. This means there is a pressing unmet need for novel therapies that have fewer side effects to assist this vulnerable group. This challenge was tackled using bioinformatic analysis of available transcriptomic data to establish a gene-based signature of the development and progression of MDS. This signature was then used to identify novel therapeutic compounds via statistically-significant connectivity mapping. This approach suggested re-purposing an existing and widely-prescribed drug, bromocriptine as a novel potential therapy in these disease settings. This drug has shown selectivity for leukemic cells as well as synergy with current therapies.

Public values for badgers, bovine TB reduction and management strategies

This paper applies an attribute-based stated choice experiment approach to estimate the value that society places on changes to the size of the badger population in England and Wales. The study was undertaken in the context of a rising incidence of bovine tuberculosis (bTB) in cattle and the government's review of current bTB control policy. This review includes consideration of culling badgers to reduce bTB in cattle, since badgers are thought to be an important wildlife reservoir for the disease. The design of the CE involved four attributes (size of badger population, cattle slaughtered due to bTB, badger management strategy and household tax) at four levels with eight choice sets of two alternatives presented to respondents. Telephone interviews were undertaken with over 400 respondents, which elicited their attitudes and preferences concerning badgers, bTB in cattle and badger management strategies. The study estimated a willingness to pay of 0.10 pound per household per year per 100,000 badgers and 1.52 pound per household per year per 10,000 cattle slaughtered due to bTB which aggregated to 22 per badger and 3298 pound per bTB slaughtered animal for all households in England and Wales. Management strategy toward badgers had a very high valuation, highlighting the emotive issue of badger culling for respondents and the importance of government policy towards badgers.

Spatio-Temporal Event Model for Cyber-Physical Systems

The emerging Cyber-Physical Systems (CPSs) are envisioned to integrate computation, communication and control with the physical world. Therefore, CPS requires close interactions between the cyber and physical worlds both in time and space. These interactions are usually governed by events, which occur in the physical world and should autonomously be reflected in the cyber-world, and actions, which are taken by the CPS as a result of detection of events and certain decision mechanisms. Both event detection and action decision operations should be performed accurately and timely to guarantee temporal and spatial correctness. This calls for a flexible architecture and task representation framework to analyze CP operations. In this paper, we explore the temporal and spatial properties of events, define a novel CPS architecture, and develop a layered spatiotemporal event model for CPS. The event is represented as a function of attribute-based, temporal, and spatial event conditions. Moreover, logical operators are used to combine different types of event conditions to capture composite events. To the best of our knowledge, this is the first event model that captures the heterogeneous characteristics of CPS for formal temporal and spatial analysis.

Partitioning the Choice Task Makes Starbucks Coffee Taste Better

Consumers are often less satisfied with a product chosen from a large assortment than a limited one. Experienced choice difficulty presumably causes this as consumers have to engage in a great number of individual comparisons. In two studies we tested whether partitioning the choice task so that consumers decided sequentially on each individual attribute may provide a solution. In a Starbucks coffee house, consumers who chose from the menu rated the coffee as less tasty when chosen from a large rather than a small assortment. However, when the consumers chose it by sequentially deciding about one attribute at a time, the effect reversed. In a tailored-suit customization, consumers who chose multiple attributes at a time were less satisfied with their suit, compared to those who chose one attribute at a time. Sequential attribute-based processing proves to be an effective strategy to reap the benefits of a large assortment.

Contribución a la automatización de sistemas de respuesta frente a intrusiones mediante ontologías

La seguridad en redes informáticas es un área que ha sido ampliamente estudiada y objeto de una extensa investigación en los últimos años. Debido al continuo incremento en la complejidad y sofisticación de los ataques informáticos, el aumento de su velocidad de difusión, y la lentitud de reacción frente a las intrusiones existente en la actualidad, se hace patente la necesidad de mecanismos de detección y respuesta a intrusiones, que detecten y además sean capaces de bloquear el ataque, y mitiguen su impacto en la medida de lo posible. Los Sistemas de Detección de Intrusiones o IDSs son tecnologías bastante maduras cuyo objetivo es detectar cualquier comportamiento malicioso que ocurra en las redes. Estos sistemas han evolucionado rápidamente en los últimos años convirtiéndose en herramientas muy maduras basadas en diferentes paradigmas, que mejoran su capacidad de detección y le otorgan un alto nivel de fiabilidad. Por otra parte, un Sistema de Respuesta a Intrusiones (IRS) es un componente de seguridad que puede estar presente en la arquitectura de una red informática, capaz de reaccionar frente a los incidentes detectados por un Sistema de Detección de Intrusiones (IDS). Por desgracia, esta tecnología no ha evolucionado al mismo ritmo que los IDSs, y la reacción contra los ataques detectados es lenta y básica, y los sistemas presentan problemas para ejecutar respuestas de forma automática. Esta tesis doctoral trata de hacer frente al problema existente en la reacción automática frente a intrusiones, mediante el uso de ontologías, lenguajes formales de especificación de comportamiento y razonadores semánticos como base de la arquitectura del sistema de un sistema de respuesta automática frente a intrusiones o AIRS. El objetivo de la aproximación es aprovechar las ventajas de las ontologías en entornos heterogéneos, además de su capacidad para especificar comportamiento sobre los objetos que representan los elementos del dominio modelado. Esta capacidad para especificar comportamiento será de gran utilidad para que el AIRS infiera la respuesta óptima frente a una intrusión en el menor tiempo posible. Abstract Security in networks is an area that has been widely studied and has been the focus of extensive research over the past few years. The number of security events is increasing, and they are each time more sophisticated, and quickly spread, and slow reaction against intrusions, there is a need for intrusion detection and response systems to dynamically adapt so as to better detect and respond to attacks in order to mitigate them or reduce their impact. Intrusion Detection Systems (IDSs) are mature technologies whose aim is detecting malicious behavior in the networks. These systems have quickly evolved and there are now very mature tools based on different paradigms (statistic anomaly-based, signature-based and hybrids) with a high level of reliability. On the other hand, Intrusion Response System (IRS) is a security technology able to react against the intrusions detected by IDS. Unfortunately, the state of the art in IRSs is not as mature as with IDSs. The reaction against intrusions is slow and simple, and these systems have difficulty detecting intrusions in real time and triggering automated responses. This dissertation is to address the existing problem in automated reactions against intrusions using ontologies, formal behaviour languages and semantic reasoners as the basis of the architecture of an automated intrusion response systems or AIRS. The aim is to take advantage of ontologies in heterogeneous environments, in addition to its ability to specify behavior of objects representing the elements of the modeling domain. This ability to specify behavior will be useful for the AIRS in the inference process of the optimum response against an intrusion, as quickly as possible.

Structure-activity relationships derived by machine learning: the use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming.

We present a general approach to forming structure-activity relationships (SARs). This approach is based on representing chemical structure by atoms and their bond connectivities in combination with the inductive logic programming (ILP) algorithm PROGOL. Existing SAR methods describe chemical structure by using attributes which are general properties of an object. It is not possible to map chemical structure directly to attribute-based descriptions, as such descriptions have no internal organization. A more natural and general way to describe chemical structure is to use a relational description, where the internal construction of the description maps that of the object described. Our atom and bond connectivities representation is a relational description. ILP algorithms can form SARs with relational descriptions. We have tested the relational approach by investigating the SARs of 230 aromatic and heteroaromatic nitro compounds. These compounds had been split previously into two subsets, 188 compounds that were amenable to regression and 42 that were not. For the 188 compounds, a SAR was found that was as accurate as the best statistical or neural network-generated SARs. The PROGOL SAR has the advantages that it did not need the use of any indicator variables handcrafted by an expert, and the generated rules were easily comprehensible. For the 42 compounds, PROGOL formed a SAR that was significantly (P < 0.025) more accurate than linear regression, quadratic regression, and back-propagation. This SAR is based on an automatically generated structural alert for mutagenicity.

Toward Never-Ending Object Learning for Robots

Thesis (Ph.D.)--University of Washington, 2016-06

Integrating structured data using property precedence

Data integration systems offer uniform access to a set of autonomous and heterogeneous data sources. One of the main challenges in data integration is reconciling semantic differences among data sources. Approaches that been used to solve this problem can be categorized as schema-based and attribute-based. Schema-based approaches use schema information to identify the semantic similarity in data; furthermore, they focus on reconciling types before reconciling attributes. In contrast, attribute-based approaches use statistical and structural information of attributes to identify the semantic similarity of data in different sources. This research examines an approach to semantic reconciliation based on integrating properties expressed at different levels of abstraction or granularity using the concept of property precedence. Property precedence reconciles the meaning of attributes by identifying similarities between attributes based on what these attributes represent in the real world. In order to use property precedence for semantic integration, we need to identify the precedence of attributes within and across data sources. The goal of this research is to develop and evaluate a method and algorithms that will identify precedence relations among attributes and build property precedence graph (PPG) that can be used to support integration.

Sharing in the Rain: Secure and Efficient Data Sharing for the Cloud

Cloud storage has rapidly become a cornerstone of many businesses and has moved from an early adopters stage to an early majority, where we typically see explosive deployments. As companies rush to join the cloud revolution, it has become vital to create the necessary tools that will effectively protect users' data from unauthorized access. Nevertheless, sharing data between multiple users' under the same domain in a secure and efficient way is not trivial. In this paper, we propose Sharing in the Rain – a protocol that allows cloud users' to securely share their data based on predefined policies. The proposed protocol is based on Attribute-Based Encryption (ABE) and allows users' to encrypt data based on certain policies and attributes. Moreover, we use a Key-Policy Attribute-Based technique through which access revocation is optimized. More precisely, we show how to securely and efficiently remove access to a file, for a certain user that is misbehaving or is no longer part of a user group, without having to decrypt and re-encrypt the original data with a new key or a new policy.

Facets of Access: a Typology of Information Dissemination Systems

In this poster, we describe a new way of conceptualizing relationships between systems for information dissemination throughout history, based on a set of basic attributes or facets. This poster illustrates one way in which these attribute-based relationships might be visualized, as a step toward illuminating novel perspectives on and comparisons between these phenomena.