1st Kassel Student Workshop on Security in Distributed Systems

With this document, we provide a compilation of in-depth discussions on some of the most current security issues in distributed systems. The six contributions have been collected and presented at the 1st Kassel Student Workshop on Security in Distributed Systems (KaSWoSDS’08). We are pleased to present a collection of papers not only shedding light on the theoretical aspects of their topics, but also being accompanied with elaborate practical examples. In Chapter 1, Stephan Opfer discusses Viruses, one of the oldest threats to system security. For years there has been an arms race between virus producers and anti-virus software providers, with no end in sight. Stefan Triller demonstrates how malicious code can be injected in a target process using a buffer overflow in Chapter 2. Websites usually store their data and user information in data bases. Like buffer overflows, the possibilities of performing SQL injection attacks targeting such data bases are left open by unwary programmers. Stephan Scheuermann gives us a deeper insight into the mechanisms behind such attacks in Chapter 3. Cross-site scripting (XSS) is a method to insert malicious code into websites viewed by other users. Michael Blumenstein explains this issue in Chapter 4. Code can be injected in other websites via XSS attacks in order to spy out data of internet users, spoofing subsumes all methods that directly involve taking on a false identity. In Chapter 5, Till Amma shows us different ways how this can be done and how it is prevented. Last but not least, cryptographic methods are used to encode confidential data in a way that even if it got in the wrong hands, the culprits cannot decode it. Over the centuries, many different ciphers have been developed, applied, and finally broken. Ilhan Glogic sketches this history in Chapter 6.

Global Optimization Algorithms and their Application to Distributed Systems

In this report, we discuss the application of global optimization and Evolutionary Computation to distributed systems. We therefore selected and classified many publications, giving an insight into the wide variety of optimization problems which arise in distributed systems. Some interesting approaches from different areas will be discussed in greater detail with the use of illustrative examples.

Applicability of Emergence Engineering to Distributed Systems Scenarios

Genetic Programming can be effectively used to create emergent behavior for a group of autonomous agents. In the process we call Offline Emergence Engineering, the behavior is at first bred in a Genetic Programming environment and then deployed to the agents in the real environment. In this article we shortly describe our approach, introduce an extended behavioral rule syntax, and discuss the impact of the expressiveness of the behavioral description to the generation success, using two scenarios in comparison: the election problem and the distributed critical section problem. We evaluate the results, formulating criteria for the applicability of our approach.

Evolving Distributed Algorithms with Genetic Programming

Distributed systems are one of the most vital components of the economy. The most prominent example is probably the internet, a constituent element of our knowledge society. During the recent years, the number of novel network types has steadily increased. Amongst others, sensor networks, distributed systems composed of tiny computational devices with scarce resources, have emerged. The further development and heterogeneous connection of such systems imposes new requirements on the software development process. Mobile and wireless networks, for instance, have to organize themselves autonomously and must be able to react to changes in the environment and to failing nodes alike. Researching new approaches for the design of distributed algorithms may lead to methods with which these requirements can be met efficiently. In this thesis, one such method is developed, tested, and discussed in respect of its practical utility. Our new design approach for distributed algorithms is based on Genetic Programming, a member of the family of evolutionary algorithms. Evolutionary algorithms are metaheuristic optimization methods which copy principles from natural evolution. They use a population of solution candidates which they try to refine step by step in order to attain optimal values for predefined objective functions. The synthesis of an algorithm with our approach starts with an analysis step in which the wanted global behavior of the distributed system is specified. From this specification, objective functions are derived which steer a Genetic Programming process where the solution candidates are distributed programs. The objective functions rate how close these programs approximate the goal behavior in multiple randomized network simulations. The evolutionary process step by step selects the most promising solution candidates and modifies and combines them with mutation and crossover operators. This way, a description of the global behavior of a distributed system is translated automatically to programs which, if executed locally on the nodes of the system, exhibit this behavior. In our work, we test six different ways for representing distributed programs, comprising adaptations and extensions of well-known Genetic Programming methods (SGP, eSGP, and LGP), one bio-inspired approach (Fraglets), and two new program representations called Rule-based Genetic Programming (RBGP, eRBGP) designed by us. We breed programs in these representations for three well-known example problems in distributed systems: election algorithms, the distributed mutual exclusion at a critical section, and the distributed computation of the greatest common divisor of a set of numbers. Synthesizing distributed programs the evolutionary way does not necessarily lead to the envisaged results. In a detailed analysis, we discuss the problematic features which make this form of Genetic Programming particularly hard. The two Rule-based Genetic Programming approaches have been developed especially in order to mitigate these difficulties. In our experiments, at least one of them (eRBGP) turned out to be a very efficient approach and in most cases, was superior to the other representations.

ACTORS: A Model of Concurrent Computation in Distributed Systems

A foundational model of concurrency is developed in this thesis. We examine issues in the design of parallel systems and show why the actor model is suitable for exploiting large-scale parallelism. Concurrency in actors is constrained only by the availability of hardware resources and by the logical dependence inherent in the computation. Unlike dataflow and functional programming, however, actors are dynamically reconfigurable and can model shared resources with changing local state. Concurrency is spawned in actors using asynchronous message-passing, pipelining, and the dynamic creation of actors. This thesis deals with some central issues in distributed computing. Specifically, problems of divergence and deadlock are addressed. For example, actors permit dynamic deadlock detection and removal. The problem of divergence is contained because independent transactions can execute concurrently and potentially infinite processes are nevertheless available for interaction.

A Distributed Model for Mobile Robot Environment-Learning and Navigation

A distributed method for mobile robot navigation, spatial learning, and path planning is presented. It is implemented on a sonar-based physical robot, Toto, consisting of three competence layers: 1) Low-level navigation: a collection of reflex-like rules resulting in emergent boundary-tracing. 2) Landmark detection: dynamically extracts landmarks from the robot's motion. 3) Map learning: constructs a distributed map of landmarks. The parallel implementation allows for localization in constant time. Spreading of activation computes both topological and physical shortest paths in linear time. The main issues addressed are: distributed, procedural, and qualitative representation and computation, emergent behaviors, dynamic landmarks, minimized communication.

Implementing Distributed Systems Using Linear Naming

Linear graph reduction is a simple computational model in which the cost of naming things is explicitly represented. The key idea is the notion of "linearity". A name is linear if it is only used once, so with linear naming you cannot create more than one outstanding reference to an entity. As a result, linear naming is cheap to support and easy to reason about. Programs can be translated into the linear graph reduction model such that linear names in the program are implemented directly as linear names in the model. Nonlinear names are supported by constructing them out of linear names. The translation thus exposes those places where the program uses names in expensive, nonlinear ways. Two applications demonstrate the utility of using linear graph reduction: First, in the area of distributed computing, linear naming makes it easy to support cheap cross-network references and highly portable data structures, Linear naming also facilitates demand driven migration of tasks and data around the network without requiring explicit guidance from the programmer. Second, linear graph reduction reveals a new characterization of the phenomenon of state. Systems in which state appears are those which depend on certain -global- system properties. State is not a localizable phenomenon, which suggests that our usual object oriented metaphor for state is flawed.

Safe Distributed Coordination of Heterogeneous Robots through Dynamic Simple Temporal Networks

Research on autonomous intelligent systems has focused on how robots can robustly carry out missions in uncertain and harsh environments with very little or no human intervention. Robotic execution languages such as RAPs, ESL, and TDL improve robustness by managing functionally redundant procedures for achieving goals. The model-based programming approach extends this by guaranteeing correctness of execution through pre-planning of non-deterministic timed threads of activities. Executing model-based programs effectively on distributed autonomous platforms requires distributing this pre-planning process. This thesis presents a distributed planner for modelbased programs whose planning and execution is distributed among agents with widely varying levels of processor power and memory resources. We make two key contributions. First, we reformulate a model-based program, which describes cooperative activities, into a hierarchical dynamic simple temporal network. This enables efficient distributed coordination of robots and supports deployment on heterogeneous robots. Second, we introduce a distributed temporal planner, called DTP, which solves hierarchical dynamic simple temporal networks with the assistance of the distributed Bellman-Ford shortest path algorithm. The implementation of DTP has been demonstrated successfully on a wide range of randomly generated examples and on a pursuer-evader challenge problem in simulation.

A Note on the Generalization Performance of Kernel Classifiers with Margin

We present distribution independent bounds on the generalization misclassification performance of a family of kernel classifiers with margin. Support Vector Machine classifiers (SVM) stem out of this class of machines. The bounds are derived through computations of the $V_gamma$ dimension of a family of loss functions where the SVM one belongs to. Bounds that use functions of margin distributions (i.e. functions of the slack variables of SVM) are derived.

Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers

The Support Vector (SV) machine is a novel type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural networks, and radial basis function (RBF) networks as special cases. In the RBF case, the SV algorithm automatically determines centers, weights and threshold such as to minimize an upper bound on the expected test error. The present study is devoted to an experimental comparison of these machines with a classical approach, where the centers are determined by $k$--means clustering and the weights are found using error backpropagation. We consider three machines, namely a classical RBF machine, an SV machine with Gaussian kernel, and a hybrid system with the centers determined by the SV method and the weights trained by error backpropagation. Our results show that on the US postal service database of handwritten digits, the SV machine achieves the highest test accuracy, followed by the hybrid approach. The SV approach is thus not only theoretically well--founded, but also superior in a practical application.

Fast Sorting on a Distributed-Memory Architecture

We consider the often-studied problem of sorting, for a parallel computer. Given an input array distributed evenly over p processors, the task is to compute the sorted output array, also distributed over the p processors. Many existing algorithms take the approach of approximately load-balancing the output, leaving each processor with Θ(n/p) elements. However, in many cases, approximate load-balancing leads to inefficiencies in both the sorting itself and in further uses of the data after sorting. We provide a deterministic parallel sorting algorithm that uses parallel selection to produce any output distribution exactly, particularly one that is perfectly load-balanced. Furthermore, when using a comparison sort, this algorithm is 1-optimal in both computation and communication. We provide an empirical study that illustrates the efficiency of exact data splitting, and shows an improvement over two sample sort algorithms.

Scaling algorithms for distributed max flow

Bibliography: p. 22-24.

Influencias de la práctica deportiva y de la orientación de meta personal en las conductas antisociales de los jóvenes.

a) Comprobar si las tendencias hacia las conductas antisociales en los jóvenes subyacen en las orientaciones de meta personal, en las variables de personalidad y en el tipo de práctica deportiva; b) Consensuar que la orientación motivacional hacia la tarea es la mejor de las dos posibles; c) Promover la orientación motivacional a la tarea por su valor como condicionante para instaurar hábitos duraderos de ocio a través de la actividad física y deportiva; d) Dismitificar la idea del deporte en sí como panacea educativa. 298 adolescentes entre 14 y 16 años. Los participantes complementaron: el Cuestionario de Percepción de Éxito POSQ para evaluar la orientación de meta personal y el Cuestionario de Personalidad de Eysenk EPQ-J para la evaluación de las tres dimensiones básicas de la personalidad: neuroticismo, extraversión y psicotísmo o dureza; y además una de sinceridad y otra de conductas antisociales. a) Se presentan datos para ilustrar la pertinencia de las metas del logro en el estudio del comportamiento motivado en contextos de actividad física y deporte; b) En la Educación Física y en el deporte escolar, la orientación a la tarea resulta la dirección pedagógica conveniente; c) El deporte competitivo en edades tempranas sacrifica la orientación a la tarea y estimula la nociva y permanente comparación con otros; d) El deporte competitivo enfatiza la implicación al yo y, por tanto, incide negativamente en la motivación intrínseca de los jóvenes; e) Los resultados mostraron diferencias estadísticamente significativas tanto en las variables de personalidad como en la orientación de meta, en función del género; f) La conducta antisocial se desarrolla mayoritariamente en los adolescentes que practican deporte federado; g) Es necesario promover la orientación motivacional a la tarea por su valor como condicionante para instaurar hábitos duraderos de ocio a través de la actividad física y deportiva.

Relaci??n del clima motivacional percibido con la orientaci??n de meta, la motivaci??n intr??nseca y las opiniones y conductas de fair play : tesis doctoral

Analizar la relaci??n entre el clima motivacional percibido, la orientaci??n de meta, la motivaci??n intr??nseca y las opiniones y comportamientos de fair play en deportistas cadetes de Primera Divisi??n de la Liga Espa??ola de F??tbol. La creencia popular de que el deporte es una actividad sociocultural que permite el enriquecimiento del individuo en el seno de la sociedad, es casi tan antigua como sus or??genes. Sin embargo, deporte y deportividad son t??rminos que, a pesar de tener un origen com??n, cada vez parecen m??s distanciados, al menos en alguna de las manifestaciones del deporte contempor??neo. A tenor de diferentes hallazgos que se recogen en el trabajo, la idea de que la participaci??n en el deporte, garantiza la formaci??n del car??cter debe replantearse. Es m??s, en al menos deportes de contacto medio y alto, puede tener efectos negativos en el razonamiento moral e incrementar la agresividad. ??Son este tipo de deportes perjudiciales en s?? mismos? ??Su l??gica interna lleva a que los deportistas acaben salt??ndose las reglas y a comportarse de una manera moralmente inaceptable? Este trabajo formula la hip??tesis de que la teor??a de meta de logro puede arrojar algo de luz sobre estas cuestiones. La hip??tesis en la que se fundamenta la investigaci??n es que el clima de Maestr??a se relaciona positivamente con la orientaci??n hacia la tarea, con la motivaci??n intr??nseca y con las opiniones y conductas pro-fair play, mientras que, por el contrario, el clima de ejecuci??n lo hace con la orientaci??n de meta hacia el ego, con la ansiedad y con las conductas anti-fair play. La muestra estuvo compuesta de un total de 82 jugadores cadetes de Primera Divisi??n de la Liga Espa??ola de F??tbol, pertenecientes al Principado de Asturias seleccionados al azar, con edades comprendidas entre los 14 y 16 a??os. Los participantes fueron sometidos voluntariamente a los siguientes cuestionarios e instrumentos de medida: el Cuestionario de Clima Motivacional Percibido en el Deporte (PMCSQ-2)de Newton y Duda (1998); el Cuestionario de Percepci??n de ??xito (POSQ) elaborado por Roberts y Balagu?? (1989); el Cuestionario de Diversi??n de los sujetos con la Pr??ctica Deportiva (CDPD) elaborado por Duda y Nicholls (1992); el Test de Motivaci??n de Logro en Educaci??n F??sica (MEF) elaborado por Nishida (1988); y la Escala de Actitudes de Fairplay (Cruz, Capdevila, Boixad??s, Pintanel, Alonso, Mimbrero y Torregrosa, 1996). Se realizaron an??lisis factoriales confirmatorios, correlaciones bivariadas y can??nicas, y an??lisis de regresi??n.

Estudio de meta-análisis sobre las causas del absentismo escolar y propuesta de un programa de prevención e intervención en la educación primaria y secundaria obligatoria.

Realizar una revisión sistemática de la literatura más relevante sobre el absentismo escolar, extrayendo las ideas de los autores más importantes, y haciendo un recorrido por la conceptualización y definiciones de absentismo escolar, tipologías, causas del absentismo y del bajo rendimiento académico. Búsqueda, localización y adquisición de los estudios más importantes; lectura y análisis; y reflexión y toma de decisiones sobre lo que han aportado los autores más relevantes sobre el tema. La detección del absentismo depende fundamentalmente de los centros educativos, ya que son los únicos que pueden cuantificarlo y certificarlo. En el momento de su detección es un problema esencialmente educativo y se debe tratar como tal. La eficacia de los procedimientos es la misma entre familias gitanas como no gitanas y dentro de aquellas, mayor cuando viven en núcleos diseminados. En muchas ocasiones la relación familia escuela no funciona debido a que los padres son quienes carecen de interés por los problemas de sus hijos en el centro escolar. Es necesario siempre el trabajo en red, con la correspondiente coordinación entre los Centros Educativos, la Consejería de Educación, Ayuntamientos, Servicios Sociales y Fiscalía de Menores. La mayoría de las instituciones educativas y sociales ha asumido la realización de protocolos donde cada una ha desarrollado algún circuito de actuación.