A Comprehensive Digital Forensic Investigation Model and Guidelines for Establishing Admissible Digital Evidence

Information technology systems are attacked by offenders using digital devices and networks to facilitate their crimes and hide their identities, creating new challenges for digital investigators. Malicious programs that exploit vulnerabilities also serve as threats to digital investigators. Since digital devices such as computers and networks are used by organisations and digital investigators, malicious programs and risky practices that may contaminate the integrity of digital evidence can lead to loss of evidence. For some reasons, digital investigators face a major challenge in preserving the integrity of digital evidence. Not only is there no definitive comprehensive model of digital forensic investigation for ensuring the reliability of digital evidence, but there has to date been no intensive research into methods of doing so. To address the issue of preserving the integrity of digital evidence, this research improves upon other digital forensic investigation model by creating a Comprehensive Digital Forensic Investigation Model (CDFIM), a model that results in an improvement in the investigation process, as well as security mechanism and guidelines during investigation. The improvement is also effected by implementing Proxy Mobile Internet Protocol version 6 (PMIPv6) with improved buffering based on Open Air Interface PIMIPv6 (OAI PMIPv6) implementation to provide reliable services during handover in Mobile Node (MN) and improve performance measures to minimize loss of data which this research identified as a factor affecting the integrity of digital evidence. The advantage of this is to present that the integrity of digital evidence can be preserved if loss of data is prevented. This research supports the integration of security mechanism and intelligent software in digital forensic investigation which assist in preserving the integrity of digital evidence by conducting experiments which carried out two different attack experiment to test CDFIM. It found that when CDFIM used security mechanism and guidelines with the investigation process, it was able to identify the attack and also ensured that the integrity of the digital evidence was preserved. It was also found that the security mechanism and guidelines incorporated in the digital investigative process are useless when the security guidelines are ignored by digital investigators, thus posing a threat to the integrity of digital evidence

GEML: A Grammatical Evolution, Machine Learning Approach to Multi-class Classification

In this paper, we propose a hybrid approach to solving multi-class problems which combines evolutionary computation with elements of traditional machine learning. The method, Grammatical Evolution Machine Learning (GEML) adapts machine learning concepts from decision tree learning and clustering methods and integrates these into a Grammatical Evolution framework. We investigate the effectiveness of GEML on several supervised, semi-supervised and unsupervised multi-class problems and demonstrate its competitive performance when compared with several well known machine learning algorithms. The GEML framework evolves human readable solutions which provide an explanation of the logic behind its classification decisions, offering a significant advantage over existing paradigms for unsupervised and semi-supervised learning. In addition we also examine the possibility of improving the performance of the algorithm through the application of several ensemble techniques

Alternative Approaches to Correction of Malapropisms in AIML Based Conversational Agents

The use of Conversational Agents (CAs) utilizing Artificial Intelligence Markup Language (AIML) has been studied in a number of disciplines. Previous research has shown a great deal of promise. It has also documented significant limitations in the abilities of these CAs. Many of these limitations are related specifically to the method employed by AIML to resolve ambiguities in the meaning and context of words. While methods exist to detect and correct common errors in spelling and grammar of sentences and queries submitted by a user, one class of input error that is particularly difficult to detect and correct is the malapropism. In this research a malapropism is defined a verbal blunder in which one word is replaced by another similar in sound but different in meaning ( malapropism, 2013). This research explored the use of alternative methods of correcting malapropisms in sentences input to AIML CAs using measures of Semantic Distance and tri-gram probabilities. Results of these alternate methods were compared against AIML CAs using only the Symbolic Reductions built into AIML. This research found that the use of the two methodologies studied here did indeed lead to a small, but measurable improvement in the performance of the CA in terms of the appropriateness of its responses as classified by human judges. However, it was also noted that in a large number of cases, the CA simply ignored the existence of a malapropism altogether in formulating its responses. In most of these cases, the interpretation and response to the user\u27s input was of such a general nature that one might question the overall efficacy of the AIML engine. The answer to this question is a matter for further study

Survey of context provisioning middleware

In the scope of ubiquitous computing, one of the key issues is the awareness of context, which includes diverse aspects of the user's situation including his activities, physical surroundings, location, emotions and social relations, device and network characteristics and their interaction with each other. This contextual knowledge is typically acquired from physical, virtual or logical sensors. To overcome problems of heterogeneity and hide complexity, a significant number of middleware approaches have been proposed for systematic and coherent access to manifold context parameters. These frameworks deal particularly with context representation, context management and reasoning, i.e. deriving abstract knowledge from raw sensor data. This article surveys not only related work in these three categories but also the required evaluation principles. © 2009-2012 IEEE

Semantic Mediation of Environmental Observation Datasets through Sensor Observation Services

A large volume of environmental observation data is being generated as a result of the observation of many properties at the Earth surface. In parallel, there exists a clear interest in accessing data from different data providers related to the same property, in order to solve concrete problems. Based on such fact, there is also an increasing interest in publishing the above data through open interfaces in the scope of Spatial Data Infraestructures. There have been important advances in the definition of open standards of the Open Geospatial Consortium (OGC) that enable interoperable access to sensor data. Among the proposed interfaces, the Sensor Observation Service (SOS) is having an important impact. We have realized that currently there is no available solution to provide integrated access to various data sources through a SOS interface. This problem shows up two main facets. On the one hand, the heterogeneity among different data sources has to be solved. On the other hand, semantic conflicts that arise during the integration process must also resolved with the help of relevant domain expert knowledge. To solve the problems, the main goal of this thesis is to design and develop a semantic data mediation framework to access any kind of environmental observation dataset, including both relational data sources and multidimensional arrays

A Supervised Learning Approach for Imbalanced Text Classification of Biomedical Literature Triage

This thesis presents the development of a machine learning system, called mycoSORT , for supporting the first step of the biological literature manual curation process, called triage. The manual triage of documents is very demanding, as researchers usually face the time-consuming and error- prone task of screening a large amount of data to identify relevant information. After querying scientific databases for keywords related to a specific subject, researchers generally find a long list of retrieved results, that has to be carefully analysed to identify only a few documents that show a potential of being relevant to the topic. Such an analysis represents a severe bottleneck in the knowledge discovery and decision-making processes in scientific research. Hence, biocurators could greatly benefit from an automatic support when performing the triage task. In order to support the triage of scientific documents, we have used a corpus of document instances manually labeled by biocurators as “selected” or “rejected”, with regards to their potential to indicate relevant information about fungal enzymes. This document collection is characterized by being large, since many results are retrieved and analysed to finally identify potential candidate documents; and also highly imbalanced, concerning the distribution of instances per relevance: the great majority of documents are labeled as rejected, while only a very small portion are labeled as selected. Using this dataset, we studied the design of a classification model to identify the most discriminative features to automate the triage of scientific literature and to tackle the imbalance between the two classes of documents. To identify the most suitable model, we performed a study of 324 classification models, which demonstrated the results of using 9 different data undersampling factors, 4 sets of features, and the evaluation of 2 feature selection methods as well as 3 machine learning algorithms. Our results demonstrated that the use of an undersampling technique is effective to handle imbalanced datasets and also help manage large document collections. We also found that the combination of undersampling and feature selection using Odds Ratio can improve the performance of our classification model. Finally, our results demonstrated that the best fitting model to support the triage of scientific documents is composed by domain relevant features, filtered by Odds Ratio scores, the use of dataset undersampling and the Logistic Model Trees algorithm

Model for WCET prediction, scheduling and task allocation for emergent agent-behaviours in real-time scenarios

[ES]Hasta el momento no se conocen modelos de tiempo real específicamente desarrollados para su uso en sistemas abiertos, como las Organizaciones Virtuales de Agentes (OVs). Convencionalmente, los modelos de tiempo real se aplican a sistemas cerrados donde todas las variables se conocen a priori. Esta tesis presenta nuevas contribuciones y la novedosa integración de agentes en tiempo real dentro de OVs. Hasta donde alcanza nuestro conocimiento, éste es el primer modelo específicamente diseñado para su aplicación en OVs con restricciones temporales estrictas. Esta tesis proporciona una nueva perspectiva que combina la apertura y dinamicidad necesarias en una OV con las restricciones de tiempo real. Ésto es una aspecto complicado ya que el primer paradigma no es estricto, como el propio término de sistema abierto indica, sin embargo, el segundo paradigma debe cumplir estrictas restricciones. En resumen, el modelo que se presenta permite definir las acciones que una OV debe llevar a cabo con un plazo concreto, considerando los cambios que pueden ocurrir durante la ejecución de un plan particular. Es una planificación de tiempo real en una OV. Otra de las principales contribuciones de esta tesis es un modelo para el cálculo del tiempo de ejecución en el peor caso (WCET). La propuesta es un modelo efectivo para calcular el peor escenario cuando un agente desea formar parte de una OV y para ello, debe incluir sus tareas o comportamientos dentro del sistema de tiempo real, es decir, se calcula el WCET de comportamientos emergentes en tiempo de ejecución. También se incluye una planificación local para cada nodo de ejecución basada en el algoritmo FPS y una distribución de tareas entre los nodos disponibles en el sistema. Para ambos modelos se usan modelos matemáticos y estadísticos avanzados para crear un mecanismo adaptable, robusto y eficiente para agentes inteligentes en OVs. El desconocimiento, pese al estudio realizado, de una plataforma para sistemas abiertos que soporte agentes con restricciones de tiempo real y los mecanismos necesarios para el control y la gestión de OVs, es la principal motivación para el desarrollo de la plataforma de agentes PANGEA+RT. PANGEA+RT es una innovadora plataforma multi-agente que proporciona soporte para la ejecución de agentes en ambientes de tiempo real. Finalmente, se presenta un caso de estudio donde robots heterogéneos colaboran para realizar tareas de vigilancia. El caso de estudio se ha desarrollado con la plataforma PANGEA+RT donde el modelo propuesto está integrado. Por tanto al final de la tesis, con este caso de estudio se obtienen los resultados y conclusiones que validan el modelo

The design and study of pedagogical paper recommendation

For learners engaging in senior-level courses, tutors in many cases would like to pick some articles as supplementary reading materials for them each week. Unlike researchers ‘Googling’ papers from the Internet, tutors, when making recommendations, should consider course syllabus and their assessment of learners along many dimensions. As such, simply ‘Googling’ articles from the Internet is far from enough. That is, learner models of each individual, including their learning interest, knowledge, goals, etc. should be considered when making paper recommendations, since the recommendation should be carried out so as to ensure that the suitability of a paper for a learner is calculated as the summation of the fitness of the appropriateness of it to help the learner in general. This type of the recommendation is called a Pedagogical Paper Recommender.In this thesis, we propose a set of recommendation methods for a Pedagogical Paper Recommender and study the various important issues surrounding it. Experimental studies confirm that making recommendations to learners in social learning environments is not the same as making recommendation to users in commercial environments such as Amazon.com. In such learning environments, learners are willing to accept items that are not interesting, yet meet their learning goals in some way or another; learners’ overall impression towards each paper is not solely dependent on the interestingness of the paper, but also other factors, such as the degree to which the paper can help to meet their ‘cognitive’ goals.It is also observed that most of the recommendation methods are scalable. Although the degree of this scalability is still unclear, we conjecture that those methods are consistent to up to 50 papers in terms of recommendation accuracy. The experiments conducted so far and suggestions made on the adoption of recommendation methods are based on the data we have collected during one semester of a course. Therefore, the generality of results needs to undergo further validation before more certain conclusion can be drawn. These follow up studies should be performed (ideally) in more semesters on the same course or related courses with more newly added papers. Then, some open issues can be further investigated. Despite these weaknesses, this study has been able to reach the research goals set out in the proposed pedagogical paper recommender which, although sounding intuitive, unfortunately has been largely ignored in the research community. Finding a ‘good’ paper is not trivial: it is not about the simple fact that the user will either accept the recommended items, or not; rather, it is a multiple step process that typically entails the users navigating the paper collections, understanding the recommended items, seeing what others like/dislike, and making decisions. Therefore, a future research goal to proceed from the study here is to design for different kinds of social navigation in order to study their respective impacts on user behavior, and how over time, user behavior feeds back to influence the system performance

Intelligent techniques for context-aware systems

Nowadays, with advances in communication technologies, researches are focused in the fields of designing new devices with increasing capabilities, implanting software frameworks or middleware to make these devices interoperable. Building better human interfaces is a challenging task and the adoption of Artificial Intelligence (AI) techniques to the process help associating semantic meaning to devices which makes possible the gesture recognition and voice recognition. This thesis is mainly concerned with the open problem in context-aware systems: the evaluation of these systems in Ambient Intelligence (AmI) environments. With regard to this issue, we argue that due to highly dynamic properties of the AmI environments, it should exist a methodology for evaluating these systems taking into account the type of scenarios. However in order to support with a solid ground for that discussion, some elements are to be discussed as well. In particular, we: • use a commercial platform that allows us to design and manage the contextual information of context- aware systems by means of a context manager included in the architecture; • analyze the formal representation of this contextual information by means of a knowledge based system (KBS); • discuss the possible methodologies to be used for modelling knowledge in KBS and our approach; • give reasons why intelligent agents is a valid technique to be applied to systems in AmI environments; • propose a generic multi-agent system (MAS) architecture that can be applied to a large class of envisaged AmI applications; • propose a multimodal user interface and its integration with our MAS; • propose an evaluation methodology for context-aware systems in AmI scenarios. The formulation of the above mentioned elements became necessary as this thesis was developed. The lack of an evaluation methodology for context-aware systems in AmI environments, where so many issues to be covered, took us to the main objective of this thesis. In this regard: • we provide an updated and exhaustive state-of-the-art of this matter; • examine the properties and characteristics of AmI scenarios; • put forward an evaluation methodology and experimentally test our methodology in AmI scenarios. ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------La Inteligencia Ambiental y los entornos inteligentes hacen hincapié en una mayor facilidad de uso, soporte de servicios más eficientes, el apoderamiento de los usuarios, y el apoyo a las interacciones humanas. En esta visión, las personas estarán rodeadas de interfaces inteligentes e intuitivas incrustados en objetos cotidianos que nos rodean y los sistemas desarrollados para este ambiente deberán reconocer y responder a la presencia de individuos de una manera invisible y transparente a ellos. Esta tesis se centra principalmente en el problema abierto en los sistemas sensibles al contexto: la evaluación de estos sistemas en los entornos de Inteligencia Ambiental. Con respecto a este tema, se argumenta que debido a las propiedades altamente dinámica de los entornos de inteligencia ambiental, debería existir una metodología para la evaluación de estos sistemas, teniendo en cuenta el tipo de escenarios. Sin embargo, con el fin de apoyar con una base sólida para la discusión, algunos elementos deben ser discutidos también. En particular, nosotros: • Usamos una plataforma comercial que nos permite diseñar y gestionar la información contextual de los sistemas sensibles al contexto a través de un gestor de contexto incluido en la arquitectura; • Analizamos la representación formal de esta información contextual a través de un sistema basado en el conocimiento (SBC); • Discutimos las posibles metodologías que se utilizarán para el modelado del conocimiento en SBC y nuestra aproximación y propuesta; • Discutimos las razones del por qué los agentes inteligentes son una técnica válida para ser aplicada a los sistemas en entornos inteligencia ambiental; • Proponemos un sistema multi-agente (SMA), con una arquitectura genérica que se puede aplicar a una gran clase de aplicaciones de inteligencia ambiental; • Proponemos una interfaz de usuario multimodales y su integración con nuestro SMA; • Proponemos una metodología de evaluación de los sistemas sensibles al contexto en los escenarios de inteligencia ambiental. La formulación de los elementos antes mencionados se hizo necesaria en la medida que esta tesis se ha desarrollado. La falta de una metodología de evaluación de los sistemas sensibles al contexto en entornos de inteligencia ambiental, donde existen tantos temas a tratar, nos llevó al objetivo principal de esta tesis. En este sentido, en esta tesis: • Proporcionamos un estado del arte actualizado y exhaustivo de este asunto; • Examinamos las propiedades y características de los escenarios de inteligencia ambiental; • Proponemos una metodología de evaluación para este tipo de sistemas y experimentalmente probamos nuestra metodología en diversos escenarios de inteligencia ambiental