This treatise represents a descriptive study of the intelligence community in the United States. It explores the ramifications of terrorism on the intelligence function, post September 11, 2001. In-depth discussions concerning the structure of the U.S. intelligence community are presented as well as a focus on the defined steps of the intelligence process: planning and directions, collection, analysis, production, and dissemination. The final aspect of this study poses questions and issues relating to the restructuring of the U.S. intelligence community in light of the Homeland Security Act of 2002.

Le développement de la recherche scientifique, jumelé à celui des TIC, a donné naissance à la société de la connaissance qui est la nôtre. Au même titre que les matières premières, le capital intellectuel, est source de développement et d’innovation, à condition de l’organiser au travers de mécanismes évitant sa dispersion pour favoriser l’émergence d’une intelligence collective. Sur la base des résultats d’une enquête, cette étude se propose de mesurer le niveau d’intégration de la connaissance dans la stratégie de développement du secteur agricole algérien. Les résultats présentés mettent en évidence que les cloisonnements entre les différents acteurs producteurs de connaissance et/ou d’information ne favorisent pas la mutualisation et les synergies. Dans ce contexte, l’Observatoire de la recherche agricole en Algérie, en cours de développement, prend toute sa place ; il vise à faire converger sur une plate forme unique, les principales sources d’information et de savoirs du secteur. / The development of scientific research, related with TIC, has given rise to the knowledge society, which is ours. As well as raw materials, intellectual capital is a source of development and innovation, provided its organization through avoiding dispersal mechanisms to favour the emergence of a collective intelligence.The author of this study, on the basis of the results inquest, wanted to measure the level of the knowledge integration in the development strategy of the agricultural sector in Algeria.The results presented highlight that the partitioning between the different actors producers of knowledge and/or information do not sub serves the mutualisation and synergy.In this context, the observatory of agricultural research in Algeria, under development, takes all its place; aiming to converge on a single platform, the main sources of information and knowledge sector.

This thesis endeavors to establish the validity of several measurements of intelligence which have been developed from inkblot perception theory.

Clustering is a fundamental data mining tool that aims to divide data into groups of similar items. Intuition about clustering reflects the ideal case -- exact data sets endowed with flawless dissimilarity between individual instances. In practice however, these cases are in the minority, and clustering applications are typically characterized by noisy data sets with approximate pairwise dissimilarities. As such, the efficacy of clustering methods necessitates robustness to perturbations. In this paper, we address foundational questions on perturbation robustness, studying to what extent can clustering techniques exhibit this desirable characteristic. Our results also demonstrate the type of cluster structures required for robustness of popular clustering paradigms. / A Thesis submitted to the Department of Computer Science in partial fulfillment of the requirements for the degree of Master of Science. / Summer Semester 2017. / May 4, 2017. / Includes bibliographical references. / Margareta Ackerman, Professor Co-Directing Thesis; Gary Tyson, Professor Co-Directing Thesis; Sonia Haiduc, Committee Member; Peixiang Zhao, Committee Member.

Typescript (photocopy). / Digitized by Kansas Correctional Industries

abstract: Rapid growth of internet and connected devices ranging from cloud systems to internet of things have raised critical concerns for securing these systems. In the recent past, security attacks on different kinds of devices have evolved in terms of complexity and diversity. One of the challenges is establishing secure communication in the network among various devices and systems. Despite being protected with authentication and encryption, the network still needs to be protected against cyber-attacks. For this, the network traffic has to be closely monitored and should detect anomalies and intrusions. Intrusion detection can be categorized as a network traffic classification problem in machine learning. Existing network traffic classification methods require a lot of training and data preprocessing, and this problem is more serious if the dataset size is huge. In addition, the machine learning and deep learning methods that have been used so far were trained on datasets that contain obsolete attacks. In this thesis, these problems are addressed by using ensemble methods applied on an up to date network attacks dataset. Ensemble methods use multiple learning algorithms to get better classification accuracy that could be obtained when the corresponding learning algorithm is applied alone. This dataset for network traffic classification has recent attack scenarios and contains over fifteen attacks. This approach shows that ensemble methods can be used to classify network traffic and detect intrusions with less training times of the model, and lesser pre-processing without feature selection. In addition, this thesis also shows that only with less than ten percent of the total features of input dataset will lead to similar accuracy that is achieved on whole dataset. This can heavily reduce the training times and classification duration in real-time scenarios. / Dissertation/Thesis / Masters Thesis Computer Science 2019

No description available.

Thèse doctorat : Discipline Informatique : Rennes, INSA : 2006. / Bibliogr.p.181-197 (173 réf.). Index.

Thesis--PlanB (M.S.)--University of Wisconsin--Stout, 2001. / Includes bibliographical references.

Abstract of Thesis (Ph. D.)--University of Illinois, 1924. / Vita.