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.

Indiana University-Purdue University Indianapolis (IUPUI) / This thesis discusses occupancy grid map, collision avoidance system and belief theory, and propose some of the latest and the most effective method such as predictive occupancy grid map, risk evaluation model and OGM role in the belief function theory with the approach of decision uncertainty according to the environment perception with the degree of belief in the driving command acceptability. Finally, how the proposed models mitigate or prevent the occurrence of the collision.

The technology exists for the migration of healthcare data from its archaic paper-based system to an electronic one and once in digital form, to be transported anywhere in the world in a matter of seconds. The advent of universally accessible healthcare data benefits all participants, but one of the outstanding problems that must be addressed is how to uniquely identify and link a patient to his or her specific medical data. To date, a few solutions to this problem have been proposed that are limited in their effectiveness. We propose the use of biometric technology within our FIRD framework in solving the unique association of a patient to his or her medical data distinctively. This would allow a patient to have real time access to all of his or her recorded healthcare information electronically whenever it is necessary, securely with minimal effort, greater effectiveness, and ease.

This dissertation proposes an iterative confidence passing (ICP) approach for parameter estimation. The dissertation describes three different algorithms that follow from this ICP approach. These three variations of the ICP approach are applied to (a) macrodiversity and user cooperation diversity reception problems, (b) the co-operative multipoint MIMO reception problem (pertinent to the LTE Advanced system scenarios), and (c) the satellite beamforming problem. The first two of these three applications are some of the significant open DSP research problems that are currently being actively pursued in academia and industry. This dissertation demonstrates a significant performance improvement that the proposed ICP approach delivers compared to the existing known techniques. The proposed ICP approach jointly estimates (and, thereby, separates) two sets of unknown parameters from the receiver measurements. For applications (a) and (b) mentioned above, one set of unknowns is comprised of the discrete-valued information-bearing transmitted symbols in a multi-channel communication system, and the other set of unknown parameters is formed by the coefficients of a Rayleigh or Rician fading channel. Application (a) is for interference-free, cooperative or macro, transmit or receive, diversity scenarios. Application (b) is for MIMO systems with interference-rich reception. Finally, application (c) is for an interference-free spacecraft array calibration system model in which both the sets of unknowns are complex continuous valued variables whose magnitude follows the Rician distribution. The algorithm described here is the outcome of an investigation for solving a difficult channel estimation problem. The difficulty of the estimation problem arises because (i) the channel of interest is intermittently observed, and (ii) the partially observed information is not directly of the channel of interest; it has dependency on another unknown and uncorrelated set of complex-valued random variables. The proposed ICP algorithmic approach for solving the above estimation problems is based on an iterative application of the Weighted Least Squares (WLS) method. The main novelty of the proposed algorithm is a back and forth exchange of the confidence or the belief values in the WLS estimates of the unknown parameters during the algorithm iterations. The confidence values of the previously obtained estimates are used to derive the estimation weights at the next iteration, which generates an improved estimate with a greater confidence. This method of iterative confidence (or belief) passing causes a bootstrapping convergence to the parameter estimates. Besides the ICP approach, several alternatives are considered to solve the above problems (a, b and c). Results of the performance simulation of the alternative methods show that the ICP algorithm outperforms all the other candidate approaches. Performance benefit is significant when the measurements (and the initial seed estimates) have non-uniform quality, e.g., when many of the measurements are either non-usable (e.g., due to shadowing or blockage) or are missing (e.g., due to instrument failures). / Ph. D.

Understanding intentionality, i.e. coding the object directedness of agents towards objects, is a fundamental component of Theory of Mind abilities. Yet it is unclear how it is perceived and coded in different species. In this thesis, we present a series of comparative studies to explore human adults' and Capuchin monkeys' ability to infer intentional objects from actions. First we studied whether capuchin monkeys and adult humans infer a potential object from observing an object-directed action. With no direct information about the goal-object, neither species inferred the object from the action. However, when the object was revealed, the monkeys retrospectively encoded the directedness of the object-directed action; unexpectedly, in an adapted version of the task adult humans did not show a similar ability. We then adapted another paradigm, originally designed by Kovács et al (2010), to examine whether the two species implicitly register the intentional relation between an agent and an object. We manipulated an animated agent and the participants' belief about a ball's presence behind a hiding screen. We found no evidence showing that humans or monkeys coded object-directedness or belief. More importantly, we failed to replicate the original results from Kovács et al's study, and through a series of follow up studies, we questioned their conclusions regarding implicit ToM understanding. We suggested that, instead of implicit ToM, results like Kovacs et al's might be interpreted as driven by “sub-mentalizing” processes, as suggested by Heyes (2014). We conclude that so called ‘implicit ToM' may be based upon the computation of intentional relations between perceived agents and objects. But, these computations might present limitations, and some results attributed to implicit ToM may in fact reflect “sub-mentalizing” processes.

Social networking sites such as Twitter, Facebook and Flickr play an important role in disseminating breaking news about natural disasters, terrorist attacks and other events. They serve as sources of first-hand information to deliver instantaneous news to the masses, since millions of users visit these sites to post and read news items regularly. Hence, by exploring e fficient mathematical techniques like Dempster-Shafer theory and Modi ed Dempster's rule of combination, we can process large amounts of data from these sites to extract useful information in a timely manner. In surveillance related applications, the objective of processing voluminous social network data is to predict events like revolutions and terrorist attacks before they unfold. By fusing the soft and often unreliable data from these sites with hard and more reliable data from sensors like radar and the Automatic Identi cation System (AIS), we can improve our event prediction capability. In this paper, we present a class of algorithms to fuse hard sensor data with soft social network data (tweets) in an e ffective manner. Preliminary results using are also presented. / Thesis / Master of Applied Science (MASc)

La reconnaissance automatique de cibles non coopératives est d’une grande importance dans divers domaines. C’est le cas pour les applications en environnement incertain aérien et maritime. Il s’avère donc nécessaire d’introduire des méthodes originales pour le traitement et l’identification des cibles radar. C’est dans ce contexte que s’inscrit notre travail. La méthodologie proposée est fondée sur le processus d’extraction de connaissance à partir de données (ECD) pour l’élaboration d’une chaine complète de reconnaissance à partir des images radar en essayant d’optimiser chaque étape de cette chaine de traitement. Les expérimentations réalisées pour constituer une base de données d’images ISAR ont été effectuées dans la chambre anéchoïque de l’ENSTA de Bretagne. Ce dispositif de mesures utilisé a l’avantage de disposer d’une maîtrise de la qualité des données représentants les entrées dans le processus de reconnaissance (ECD). Nous avons ainsi étudié les étapes composites de ce processus de l’acquisition jusqu’à l’interprétation et l’évaluation de résultats de reconnaissance. En particulier, nous nous sommes concentrés sur l’étape centrale dédiée à la fouille de données considérée comme le cœur du processus développé. Cette étape est composée de deux phases principales : une porte sur la classification et l’autre sur la fusion des résultats des classifieurs, cette dernière est nommée fusion décisionnelle. Dans ce cadre, nous avons montré que cette dernière phase joue un rôle important dans l’amélioration des résultats pour la prise de décision tout en prenant en compte les imperfections liées aux données radar, notamment l’incertitude et l’imprécision. Les résultats obtenus en utilisant d’une part les différentes techniques de classification (kppv, SVM et PMC), et d’autre part celles de de fusion décisionnelle (Bayes, vote, théorie de croyance, fusion floue) font l’objet d’une étude analytique et comparative en termes de performances. / The automatic recognition of non-cooperative targets is very important in various fields. This is the case for applications in aviation and maritime uncertain environment. Therefore, it’s necessary to introduce innovative methods for radar targets treatment and identification.The proposed methodology is based on the Knowledge Discovery from Data process (KDD) for a complete chain development of radar images recognition by trying to optimize every step of the processing chain.The experimental system used is based on an ISAR image acquisition system in the anechoic chamber of ENSTA Bretagne. This system has allowed controlling the quality of the entries in the recognition process (KDD). We studied the stages of the composite system from acquisition to interpretation and evaluation of results. We focused on the center stage; data mining considered as the heart of the system. This step is composed of two main phases: classification and the results of classifiers combination called decisional fusion. We have shown that this last phase improves results for decision making by taking into account the imperfections related to radar data, including uncertainty and imprecision.The results across different classification techniques as a first step (kNN, SVM and MCP) and decision fusion in a second time (Bayes, majority vote, belief theory, fuzzy fusion) are subject of an analytical and comparative study in terms of performance.