No description available.

The increasing number of network security related incidents has made it necessary for the organizations to actively protect their sensitive data with network intrusion detection systems (IDSs). Detecting intrusion in a distributed network from outside network segment as well as from inside is a difficult problem. IDSs are expected to analyze a large volume of data while not placing a significant added load on the monitoring systems and networks. This requires good data mining strategies which take less time and give accurate results. In this study, a novel hybrid layered multiagent-based intrusion detection system is created, particularly with the support of a multi-class supervised classification technique. In agent-based IDS, there is no central control and therefore no central point of failure. Agents can detect and take predefined actions against malicious activities, which can be detected with the help of data mining techniques. The proposed IDS shows superior performance compared to central sniffing IDS techniques, and saves network resources compared to other distributed IDSs with mobile agents that activate too many sniffers causing bottlenecks in the network. This is one of the major motivations to use a distributed model based on a multiagent platform along with a supervised classification technique. Applying multiagent technology to the management of network security is a challenging task since it requires the management on different time instances and has many interactions. To facilitate information exchange between different agents in the proposed hybrid layered multiagent architecture, a low cost and low response time agent communication protocol is developed to tackle the issues typically associated with a distributed multiagent system, such as poor system performance, excessive processing power requirement, and long delays. The bandwidth and response time performance of the proposed end-to-end system is investigated through the simulation of the proposed agent communication protocol on our private LAN testbed called Hierarchical Agent Network for Intrusion Detection Systems (HAN-IDS). The simulation results show that this system is efficient and extensible since it consumes negligible bandwidth with low cost and low response time on the network.

Doctor of Philosophy / Computing and Information Sciences / Scott A. DeLoach / As sensor prices drop and computing devices continue to become more compact and powerful, computing capabilities are being embedded throughout our physical environment. Connecting these devices in cyber-physical systems (CPS) enables applications with significant societal impact and economic benefit. However, engineering CPS poses modeling, architecture, and engineering challenges and, to fully realize the desired benefits, many outstanding challenges must be addressed. For the cyber parts of CPS, two decades of work in the design of autonomous agents and multiagent systems (MAS) offers design principles for distributed intelligent systems and formalizations for agent-oriented software engineering (AOSE). MAS foundations offer a natural fit for enabling distributed interacting devices. In some cases, complex control structures such as holarchies can be advantageous. These can motivate complex organizational strategies when implementing such systems with a MAS, and some designs may require agents to act in multiple groups simultaneously. Such agents must be able to manage their multiple associations and assignments in a consistent and unambiguous way. This thesis shows how designing agents as systems of intelligent subagents offers a reusable and practical approach to designing complex systems. It presents a set of flexible, reusable components developed for OBAA++, an organization-based architecture for single-group MAS, and shows how these components were used to develop the Adaptive Architecture for Systems of Intelligent Systems (AASIS) to enable multigroup agents suitable for complex, multigroup MAS. This work illustrates the reusability and flexibility of the approach by using AASIS to simulate a CPS for an intelligent power distribution system (IPDS) operating two multigroup MAS concurrently: one providing continuous voltage control and a second conducting discrete power auctions near sources of distributed generation.

Nous présentons dans cette thèse un modèle pour la coordination de la parole dans des interactions dyadiques utilisateur-agent. Selon une approche courante, coordonner la parole reviendrait à éviter les recouvrements de parole et à minimiser les moments de silence entre deux tours, ceci pour rendre plus fluide l’interaction avec l’agent et améliorer l’expérience de l’utilisateur en interaction dialogique avec l’agent. Les interactions humaines montrent néanmoins une coordination plus complexe avec des recouvrements de parole compétitifs ou non compétitifs et des moments de silences longs. Selon notre approche, c’est en permettant cette diversité des situations que nous verrons émerger, entre l’utilisateur et l’agent, une interaction plus fluide et plus crédible, améliorant l’expérience de l’utilisateur avec l’agent. Les échanges de paroles sont néanmoins, par nature, complexes, la coordination se faisant par l’interaction entre locuteur et auditeur plus que par un participant en particulier. Pour capturer cette complexité, nous avons élaboré un modèle mettant l’accent sur une coordination de la parole basée sur un couplage sensorimoteur continu. Sur la base de ce couplage sensorimoteur, le comportement de l’agent n’est pas entièrement contrôlé par ce dernier, mais est émergent de l’interaction entre les participants. Nous montrons la capacité de notre modèle à faire émerger les différentes situations liées à la coordination de la parole humaine à la fois dans une interaction entre deux agents et dans une interaction utilisateur-agent. / In this thesis, we present a model for the coordination of speaking turns in dyadic interactions between users and agents. According to a common view, to coordinate turns means avoiding overlaps and reduces silences between turns. By optimizing turn transitions between users and agents, the user’s experience is expected to be improved. However, observations of human conversations show a more complex coordination of speaking turns between users and agents: awkward silences and overlaps, competitive or not, are common. In order to improve the credibility and the naturalness of the interaction, we must observe the same variability of situations in a user-agent interaction. Nevertheless, coordination of speaking turns is, by nature, complex, the coordination is managed by the interaction between participants more than controlled by one participant alone. To capture this complexity, we elaborated a model emphasizing the continuous sensory-motor coupling existing between the user and the agent. As a result of this sensory-motor coupling, the behavior of the agent is not entirely controlled by the agent but is an emergent property of the interaction between the user and the agent. We show the capacity of our model to make emerge the different situations linked to the coordination of speaking turns in interactions between two agents and between one user and one agent.

Submitted by Rosivalda Pereira (mrs.pereira@ufma.br) on 2017-09-22T18:42:19Z No. of bitstreams: 1 AdrianaCosta.pdf: 3838428 bytes, checksum: 2c98d9d837cb8ba187aa038e80b2d304 (MD5) / Made available in DSpace on 2017-09-22T18:42:19Z (GMT). No. of bitstreams: 1 AdrianaCosta.pdf: 3838428 bytes, checksum: 2c98d9d837cb8ba187aa038e80b2d304 (MD5) Previous issue date: 2017-08-14 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Fundação de Amparo à Pesquisa e ao Desenvolvimento Científico e Tecnológico do Maranhão / Software agents represent an evolution of traditional software entities, having the ability to control their own behavior and acting with autonomy. Typically, software agents act reactively, where actions and perceptions are predefined at design time, or in a deliberative way, where the corresponding action for a given perception is found at run time through reasoning. Deliberative agents do not need all knowledge to be predefined; on the contrary, from an initial knowledge they can infer new knowledge. However, to find an action appropriate to a particular perception, they take a long time, generating a high computational cost. As a solution to this problem, a hybrid architecture with learning for the development of hybrid software agents is presented in this work. Hybrid agents combine reactive and deliberative behavior taking advantage of the speed of reactive behavior and the reasoning capability of the deliberative one are a better option for structuring software agents. The main advantages of the proposed architecture are learning of the reactive behavior, faster and more efficient, through the interactions of the agent with its environment and its consequent adaptability to the environment. The agent adapts to the environment as it learns new reactive behavior from frequent deliberative behavior. The proposed architecture was evaluated through the development of case studies in the information security domain using case-based reasoning, ontologies for the representation of domain knowledge and supervised learning for automatic generation of reactive rules. Results obtained with the case studies performed confirmed a greater effectiveness and a shorter response time of the hybrid agent with learning regarding both the reactive or deliberative agent as well as a hybrid agent without learning in the intrusion detection in computer networks domain. From the specification and evaluation of the hybrid architecture with supervised learning in the Information Security domain, a reference architecture for the development of hybrid agents with learning was generalized. In future works, we intend to evaluate this reference architecture in other domains, with other types of reasoning and learning techniques to evaluate its impact on the productivity and quality of the development of hybrid software agents. / Os agentes de software representam uma evolução do software tradicional, tendo a capacidade de controlar seu próprio comportamento e agir com autonomia. Tipicamente, os agentes de software agem de forma reativa, onde as percepções e ações são predefinidas no momento da sua concepção, ou de forma deliberativa, onde a ação correspondente para uma determinada percepção é encontrada em tempo de execução através de um processo de raciocínio. Os agentes deliberativos não necessitam que todo o conhecimento seja predefinido, ao contrário, a partir de um conhecimento inicial eles conseguem inferir novo conhecimento. Todavia, em muitos casos, para encontrar uma ação apropriada a uma determinada percepção eles levam muito tempo, gerando um alto custo computacional. Como solução a esse problema, apresentamos neste trabalho uma arquitetura híbrida com aprendizagem para o desenvolvimento de agentes de software híbridos. Os agentes híbridos, que combinam comportamento reativo e deliberativo, são uma opção melhor para estruturar os agentes de software. As principais vantagens da arquitetura tese é o aprendizado do comportamento reativo, mais rápido e eficiente, através de interações do agente com o seu ambiente e a sua consequente adaptabilidade ao ambiente. O agente se adapta ao ambiente na medida em que aprende novo comportamento reativo a partir de comportamento deliberativo frequente. A arquitetura tese foi avaliada através do desenvolvimento de estudos de casos no domínio da segurança da informação utilizando o raciocínio baseado em casos, ontologias para a representação do conhecimento do domínio de estudo e aprendizagem supervisionada para geração automática de regras reativas. Os resultados obtidos com os estudos de casos realizados confirmaram uma efetividade maior e um menor tempo de resposta do agente híbrido com aprendizagem em relação tanto ao comportamento isolado de um agente reativo ou deliberativo bem como de um agente híbrido sem aprendizagem no domínio da detecção de intrusões em redes de computadores. A partir da especificação e avaliação da arquitetura híbrida com aprendizagem supervisionada no domínio da Segurança da Informação, foi generalizada uma arquitetura de referência para o desenvolvimento de agentes híbridos com aprendizagem. Em trabalhos futuros, pretende-se avaliar esta arquitetura de referência em outros domínios, com outros tipos de raciocínio e técnicas de aprendizagem para avaliar o seu impacto na produtividade e qualidade do desenvolvimento de agentes de software híbridos.