Tighza (ou Jebel Aouam) est un important district minier hercynien exploité pour Pb-Ag-Zn mais également riche en or et tungstène. Les structures aurifères majeures sont des filons quartzeux en transpression sénestre N70 °E à N 110 °E qui montrent 1) un quartz hyalin Q1a à tungstène, cataclasé en quartz saccharoïde Q1b, 2) un quartz Q2 (rare) à sulfures (löllingite -arsénopyrite) et or, et 3) un stade à pyrrhotite -sphalérite (chalcopyrite). La zonalité périgranitique est très nette, les teneurs en or diminuant rapidement en s'éloignant du granite de la mine. Les skarns du granite du mispickel montrent 4 stades de développement à andradite dominante vésuvianite fluorée, hédenbergite, hydrogrossulaire fluoré, et actinote fluorée. Les sulfures marquent l'infiltration de fluides minéralisés aurifères, et non pas une évolution rétrograde du skarn. Les quartz hyalin et saccharoïde montrent des fluides aquo-carboniques calciques, à H2S et cuivre, de composition constante (X H20 : 81 mole%; X C02: 18 mole%; X NaCI : 1 mole % environ). La pression de piégeage est de l'ordre de 1,5 à 2 kbars avec une température moyenne de 300 à 350°C. Ces inclusions précoces subissent des explosions, traduisant une remontée du bloc de Tighza et amenant la recristallisation d'un quartz saccharoïde Q2, à inclusions multiphasées sursaturées. Les datations 40Ar/39Ar démontrent le synchronisme à 286 +/-1 Ma du granite de la mine, des skarnoïdes à tungstène et des filons à scheelite -molybdénite, et suggèrent l'antériorité du filon aurifère W1 nord (291,8 +/- 0,3 Ma) par rapport au granite de la mine. La morphologie des minéralisations aurifères semble fonction de la perméabilité et de la réactivité de l'encaissant: une fracture précoce ouverte réactivée donne les filons aurifères, tandis qu'un encaissant plus réactif donne les disséminations aurifères et les skarns. Les arguments de terrain et les compositions isotopiques du plomb excluent une parenté entre les minéralisations pIombo-argentifères et aurifères.

An excessive curve of Spee (COS) is a common orthodontic finding, particularly in patients with a deep overbite (OB). The purpose of this analysis was to evaluate COS leveling and OB correction in patients treated with IncognitoTM, a customized lingual appliance system. Pre- and post-treatment cephalometric radiographs were compared for 34 patients with a deep OB and excessive COS treated with IncognitoTM. The mean pre- and post-treatment COS was 1.78 mm (SD: 0.36 mm) and 0.37 mm (SD: 0.41 mm) respectively, indicating a significant amount of leveling (-1.41 mm, SD: 0.49 mm, p<0.001). The mean pre- and post-treatment OB was 5.80 mm (SD: 1.26 mm) and 2.91 mm (SD: 0.86 mm) respectively, demonstrating a significant reduction in OB (-2.89 mm, SD 1.27 mm, p<0.001). COS and OB correction was accomplished by incisor proclination, and a greater (but not significantly different) amount of mandibular incisor intrusion versus premolar and molar extrusion.

In this study the saltwater intrusion in coastal aquifers is briefly described and the prevention techniques are discussed. By using two computer models SWI and SHARP, the movement of freshwater and saltwater is analyzed on hypothetical cases and on one real aquifer in Marmaris in the coast of Mediterranean Region. Artificial recharge and subsurface barrier techniques are applied to hypothetical cases as means of controling the saltwater intrusion. The performance of strip recharge and subsurface barrier in reducing the salt water intrusion is analyzed by simulation of the groundwater flow with the codes mentioned above. The results obtained are compared with each other. The results of hypothetical cases are relatively in good agreement. For the real aquifer the results show discrepancy that cannot be ignored.

With the increasing use of Intrusion Detection Systems (IDS) as a core component of network security, a vast array of competing products have appeared to fulfil the role of reliably detecting potential breaches of security in a network. The domain of detecting intrusions is large. This leads to products which are better at detecting some intrusions than others, and so to the use of multiple different types of IDS within a network. This typical usage, combined with the common practice of using IDS at multiple points in the network, requires sophisticated management of heterogenous alerts from multiple sources. This management should enable correlation of alerts with the goal of better detecting attacks, and reducing the monitoring workload on administrators. This thesis presents an architecture utilising commodity components and the Intrusion Detection Message Exchange Format (IDMEF) to enable this type of alert management. A signature scheme for the specification of patterns of alerts that indicate multi-step attacks is given, and a methodology for analysing alerts using the architecture that was developed. The final outcomes are a signature system and collection of tools integrated in a GUI management interface to aid in the detection of attacks, and the results of utilising these tools on a series of experiments in attack detection.

Network security is an important domain in the field of computer engineering. Sensitive information flowing across computer networks is vulnerable to potential threats, therefore it is important to ensure their security. Wireless Mesh Networks (WMNs) are self-organized networks deployed in small proximity which have an wireless ad-hoc mesh topology. While they are cost effective and easy to deploy, they are extremely vulnerable to network intrusions due to no central switch or router. However, they can be secured using cryptographic techniques, firewalls or Demilitarized Zones (DMZs). Intrusion Detection Systems (IDSs) are used as a secondary line-of-defence in computer networks from possible intrusions. This thesis proposes a framework for a Hybrid Intrusion Detection System (HIDS) for WMN. / Graduate

THIAGO, Vinicius da Silva. Arquitetura multi-agentes para detecção de intrusão distribuida. 2012. 101 f. Dissertação (Mestrado em ciência da computação)- Universidade Federal do Ceará, Fortaleza-CE, 2012. / Submitted by Elineudson Ribeiro (elineudsonr@gmail.com) on 2016-07-12T18:46:20Z No. of bitstreams: 1 2012_dis_vsthiago.pdf: 6549625 bytes, checksum: 57aa3af5bfc03f007d9bea048190cfb7 (MD5) / Approved for entry into archive by Rocilda Sales (rocilda@ufc.br) on 2016-07-22T12:47:27Z (GMT) No. of bitstreams: 1 2012_dis_vsthiago.pdf: 6549625 bytes, checksum: 57aa3af5bfc03f007d9bea048190cfb7 (MD5) / Made available in DSpace on 2016-07-22T12:47:27Z (GMT). No. of bitstreams: 1 2012_dis_vsthiago.pdf: 6549625 bytes, checksum: 57aa3af5bfc03f007d9bea048190cfb7 (MD5) Previous issue date: 2012 / The growing concern about information security in computer networks is responsible for constantly producing new ways to defend them. Within this context, the development of new ways of intrusion detection plays an important role in protecting the information. Detection systems must be efficient and, at the same time, must not overload the network or the processing capabilities of the nodes within it. In order to be effective, a system must base its decisions on as many sources of information as possible and organize knowledge in a way that allows a functional communication between those sources. This dissertation describes the proposal for a Distributed Intrusion Detection System architecture that uses mobile agents and an ontology for information sharing. Mobile agents provide a convenient way to distribute the detection process, enabling peer to peer cooperation between network nodes without generating much additional traffic. The ontology provides an organized way of storing and sharing knowledge. The proposed architecture has been implemented using the Java programming language and JADE framework and a test laboratory has been assembled to verify the operation of the system. The tests results confirmed that a distributed multi-agent architecture that uses an ontology can be effective in detecting attacks on networks and systems. / A crescente preocupação com a segurança da informação em redes de computadores é responsável por produzir constantemente novas formas de defender as mesmas. Dentro desse contexto, o desenvolvimento de novas formas de detecção de intrusão assume um papel muito importante na proteção das informações. Os sistemas de detecção de intrusão precisam ser eficientes e ao mesmo tempo não devem sobrecarregar a rede ou a capacidade de processamento dos nós que a compõem. Com o objetivo de ser eficiente, um sistema deve basear as suas decisões em tantas fontes de informação quanto forem possíveis e organizar o conhecimento de forma que permita uma comunicação funcional entre essas fontes. Este trabalho descreve a proposta de uma arquitetura de um Sistema de Detecção de Intrusão Distribuído que utiliza agentes móveis e uma ontologia para o compartilhamento da informação. Os agentes móveis proporcionam uma maneira prática de distribuir o processo de detecção, possibilitando cooperação ponto a ponto entre os nós da rede sem gerar muito tráfego adicional. A ontologia fornece uma maneira organizada de armazenar e compartilhar o conhecimento. A arquitetura proposta foi implementada utilizando a linguagem de programação Java e o framework JADE e foi montado um laboratório de testes para verificar o funcionamento do sistema. Os resultados obtidos com os testes confirmaram que uma arquitetura distribuída multi-agentes que faz uso de uma ontologia pode ser eficiente na detecção de ataques a redes e sistemas.

Layered mafic intrusions represent an important aspect of magmatism on earth and have occurred from Archean to present times. Literature on the geochemistry and petrology of these intrusions abounds but their physical properties, which could provide significant constraints on their formation, have seldom been investigated. Classic petrological methods such as whole-rock geochemistry, textural analysis and mineral chemistry have been applied to several intrusions of various ages. Most of these methods are relatively expensive or time intensive which limits high resolution studies. On the contrary, magnetic methods are typically inexpensive and fast and have been successfully applied to various occurrences of mafic rocks. In this study, several magnetic methods have been applied to a 600 m-long continuous borehole core drilled through one of the world's largest layered mafic intrusion, the Great Dyke of Zimbabwe. The main goal of this study is to constrain the magmatic history of the intrusion. More specifically, it is important to determine if the intrusion functioned as an open system, characterized by multiple magma pulses, or as a closed system, undergoing differentiation after a single magmatic pulse. The magnetic methods have also been validated by other independent approaches including image analysis, and electron microprobe. This study demonstrates that magnetic methods can be used to rapidly obtain critical information on the internal structure of this type of intrusion before applying more costly chemical analyses. The main scientific result of this study is to document the closed system nature of the Great Dyke of Zimbabwe, at least throughout the sequence investigated.

Kyoto University (京都大学) / 0048 / 新制・課程博士 / 博士(工学) / 甲第14599号 / 工博第3067号 / 新制||工||1456(附属図書館) / 26951 / UT51-2009-D311 / 京都大学大学院工学研究科都市環境工学専攻 / (主査)教授 大西 有三, 教授 間瀬 肇, 准教授 西山 哲 / 学位規則第4条第1項該当

Intrusion detection is the essential part of network security in combating against illegal network access or malicious cyberattacks. Due to the constantly evolving nature of cyber attacks, it has been a technical challenge for an intrusion detection system (IDS) to effectively recognize unknown attacks or known attacks with inadequate training data. Therefore in this dissertation work, an innovative two-stage classifier is developed for accurately and efficiently detecting both unknown attacks and known attacks with insufficient or inaccurate training information. The novel two-stage fuzzy classification scheme is based on advanced machine learning techniques specifically for handling the ambiguity of traffic connections and network data. In the first stage of the classification, a fuzzy C-means (FCM) algorithm is employed to softly compute and optimize clustering centers of the training datasets with some degree of fuzziness counting for feature inaccuracy and ambiguity in the training data. Subsequently, a distance-weighted k-NN (k-nearest neighbors) classifier, combined with the Dempster-Shafer Theory (DST), is introduced to assess the belief functions and pignistic probabilities of the incoming data associated with each of known classes to further address the data uncertainty issue in the cyberattack data. In the second stage of the proposed classification algorithm, a subsequent classification scheme is implemented based on the obtained pignistic probabilities and their entropy functions to determine if the input data are normal, one of the known attacks or an unknown attack. Secondly, to strengthen the robustness to attacks, we form the three-layer hierarchy ensemble classifier based on the FCM weighted k-NN DST classifier to have more precise inferences than those made by a single classifier. The proposed intrusion detection algorithm is evaluated through the application of the KDD’99 datasets and their variants containing known and unknown attacks. The experimental results show that the new two-stage fuzzy KNN-DST classifier outperforms other well-known classifiers in intrusion detection and is especially effective in detecting unknown attacks.

With the rapid growth of the Internet, computer attacks are increasing at a fast pace and can easily cause millions of dollar in damage to an organization. Detecting these attacks is an important issue of computer security. There are many types of attacks and they fall into four main categories, Denial of Service (DoS) attacks, Probe, User to Root (U2R) attacks, and Remote to Local (R2L) attacks. Within these categories, DoS and Probe attacks continuously show up with greater frequency in a short period of time when they attack systems. They are different from the normal traffic data and can be easily separated from normal activities. On the contrary, U2R and R2L attacks are embedded in the data portions of the packets and normally involve only a single connection. It becomes difficult to achieve satisfactory detection accuracy for detecting these two attacks. Therefore, we focus on studying the ambiguity problem between normal activities and U2R/R2L attacks. The goal is to build a detection system that can accurately and quickly detect these two attacks. In this dissertation, we design a two-phase intrusion detection approach. In the first phase, a correlation-based feature selection algorithm is proposed to advance the speed of detection. Features with poor prediction ability for the signatures of attacks and features inter-correlated with one or more other features are considered redundant. Such features are removed and only indispensable information about the original feature space remains. In the second phase, we develop an ensemble intrusion detection system to achieve accurate detection performance. The proposed method includes multiple feature selecting intrusion detectors and a data mining intrusion detector. The former ones consist of a set of detectors, and each of them uses a fuzzy clustering technique and belief theory to solve the ambiguity problem. The latter one applies data mining technique to automatically extract computer users’ normal behavior from training network traffic data. The final decision is a combination of the outputs of feature selecting and data mining detectors. The experimental results indicate that our ensemble approach not only significantly reduces the detection time but also effectively detect U2R and R2L attacks that contain degrees of ambiguous information.