The importance of properly securing an organization’s information and computing resources has become paramount in modern business. Since the advent of the Internet, securing this organizational information has become increasingly difficult. Organizations deploy many security mechanisms in the protection of their data, intrusion detection systems in particular have an increasingly valuable role to play, and as networks grow, administrators need better ways to monitor their systems. Currently, many intrusion detection systems lack the means to accurately monitor and report on wireless segments within the corporate network. This dissertation proposes an extension to the NeGPAIM model, known as NeGPAIM-W, which allows for the accurate detection of attacks originating on wireless network segments. The NeGPAIM-W model is able to detect both wired and wireless based attacks, and with the extensions to the original model mentioned previously, also provide for correlation of intrusion attacks sourced on both wired and wireless network segments. This provides for a holistic detection strategy for an organization. This has been accomplished with the use of Fuzzy logic and neural networks utilized in the detection of attacks. The model works on the assumption that each user has, and leaves, a unique footprint on a computer system. Thus, all intrusive behaviour on the system and networks which support it, can be traced back to the user account which was used to perform the intrusive behavior.

Artificial neural networks are systems composed of interconnected simple computing units known as artificial neurons which simulate some properties of their biological counterparts. They have been developed and studied for understanding how brains function, and for computational purposes. In order to use a neural network for computation, the network has to be designed in such a way that it performs a useful function. Currently, the most popular method of designing a network to perform a function is to adjust the parameters of a specified network until the network approximates the input-output behaviour of the function. Although some analytical knowledge about the function is sometimes available or obtainable, it is usually not used. Some neural network paradigms exist where such knowledge is utilized; however, there is no systematical method to do so. The objective of this research is to develop such a method. A systematic method of neural network design, which we call algebraic derivation methodology, is proposed and developed in this thesis. It is developed with an emphasis on designing neural networks to implement image processing algorithms. A key feature of this methodology is that neurons and neural networks are represented symbolically such that a network can be algebraically derived from a given function and the resulting network can be simplified. By simplification we mean finding an equivalent network (i.e., performing the same function) with fewer layers and fewer neurons. A type of neural networks, which we call LQT networks, are chosen for implementing image processing algorithms. Theorems for simplifying such networks are developed. Procedures for deriving such networks to realize both single-input and multiple-input functions are given. To show the merits of the algebraic derivation methodology, LQT networks for implementing some well-known algorithms in image processing and some other areas are developed by using the above mentioned theorems and procedures. Most of these networks are the first known such neural network models; in the case there are other known network models, our networks have the same or better performance in terms of computation time. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate

Reinforcement learning algorithms comprise a class of learning algorithms for neural networks. Reinforcement learning is distinguished from other classes by the type of problems that it is intended to solve. It is used for learning input-output mappings where the desired outputs are not known and only a scalar reinforcement value is available. Primary Reinforcement Learning (PRL) is a core component of the most actively researched form of reinforcement learning. The issues surrounding the convergence characteristics of PRL are considered in this thesis. There have been no convergence proofs for any kind of networks learning under PRL. A convergence theorem is proved in this thesis, showing that under some conditions, a particular reinforcement learning algorithm, the A[formula omitted] algorithm, will train a single-layer network correctly. The theorem is demonstrated with a series of simulations. A new PRL algorithm is proposed to deal with the training of multiple layer, binary output networks with continuous inputs. This is a more difficult learning problem than with binary inputs. The new algorithm is shown to be able to successfully train a network with multiple outputs when the environment conforms to the conditions of the convergence theorem for a single-layer network. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate

In general, it is difficult to automatically detect faults within large scale engineering plants early during their onset. This is due to a number of factors including the large number of components typically present in such plants and the complex interactions of these components, which are typically poorly understood. Traditionally, fault detection within these plants has been performed through the use of status monitoring systems employing limit checking fault detection. In this approach, upper and lower bounds are placed on what is prescribed as “normal” behaviour for each of the plant's collected status data signals and fault flags are generated if and when the given status data signal exceeds either of its bounds. This approach tends to generate relatively large numbers of false alarms, due to the technique's inability to model known signal dependencies, and it also tends to produce inconsistent fault flags, in the sense that the flags do not tend to be produced throughout the “fault” event. The limit checking approach also is not particularly adept at early fault detection tasks since as long as the given status data signal remains between the upper and lower bounds any signal behaviour is deemed as acceptable. Hence, behavioural changes in the status data signals go undetected until their severity is such that either the upper or lower bounds are exceeded. In this dissertation, two novel fault detection methodologies are proposed which are better suited to the early fault detection task than traditional limit checking. The first technique is directed at modeling of signals exhibiting unknown linear dependencies. This detection system utilizes fuzzy membership functions to model signal behaviour and through this modelling approach fault detection bounds are generated which meet a prescribed probability of false alarm rate. The second technique is directed at modelling signals exhibiting unknown non-linear, dynamic dependencies. This system utilizes recurrent neural network technology to model the signal behaviours and prescribed statistical methods are employed to determine appropriate fault detection thresholds. Both of these detection systems have been designed to be able to be retrofitted into existing industrial status monitoring system and, as such, they have been designed to achieve good modelling performance in spite of the coarsely quantized status data signals which are typical of industrial status monitoring systems constructed to employ limit checking. The fault detection properties of the proposed fault detection systems were also compared to an in situ limit checking fault detection system for a set of real-world data obtained from an operational large scale engineering plant. This comparison showed that both of the proposed fault detection systems achieved marked improvements over traditional limit checking both in terms of their false alarm rates and their fault detection sensitivities. / Graduate

Magister Scientiae - MSc / Huge amounts of data are being collected as a result of the increased use of mobile telecommunications. Insight into information and knowledge derived from these databases can give operators a competitive edge in terms of customer care and retention, marketing and fraud detection. One of the strategies for fraud detection checks for signs of questionable changes in user behavior. Although the intentions of the mobile phone users cannot be observed, their intentions are reflected in the call data which define usage patterns. Over a period of time, an individual phone generates a large pattern of use. While call data are recorded for subscribers for billing purposes, we are making no prior assumptions about the data indicative of fraudulent call patterns, i.e. the calls made for billing purpose are unlabeled. Further analysis is thus, required to be able to isolate fraudulent usage. An unsupervised learning algorithm can analyse and cluster call patterns for each subscriber in order to facilitate the fraud detection process. This research investigates the unsupervised learning potentials of two neural networks for the profiling of calls made by users over a period of time in a mobile telecommunication network. Our study provides a comparative analysis and application of Self-Organizing Maps (SOM) and Long Short-Term Memory (LSTM) recurrent neural networks algorithms to user call data records in order to conduct a descriptive data mining on users call patterns. Our investigation shows the learning ability of both techniques to discriminate user call patterns; the LSTM recurrent neural network algorithm providing a better discrimination than the SOM algorithm in terms of long time series modelling. LSTM discriminates different types of temporal sequences and groups them according to a variety of features. The ordered features can later be interpreted and labeled according to specific requirements of the mobile service provider. Thus, suspicious call behaviours are isolated within the mobile telecommunication network and can be used to to identify fraudulent call patterns. We give results using masked call data from a real mobile telecommunication network. / South Africa

Artificial intelligence powered by deep neural networks has seen tremendous improvements in the last decade, achieving superhuman performance on a diverse range of tasks. Many worry that it can one day develop the ability to recursively self-improve itself, leading to an intelligence explosion known as the Singularity. Autogenerative networks, or neural networks generating neural networks, is one major plausible pathway towards realizing this possibility. The object of this thesis is to study various challenges and applications of small-scale autogenerative networks in domains such as artificial life, reinforcement learning, neural network initialization and optimization, gradient-based meta-learning, and logical networks. Chapters 2 and 3 describe novel mechanisms for generating neural network weights and embeddings. Chapters 4 and 5 identify problems and propose solutions to fix optimization difficulties in differentiable mechanisms of neural network generation known as Hypernetworks. Chapters 6 and 7 study implicit models of network generation like backpropagating through gradient descent itself and integrating discrete solvers into continuous functions. Together, the chapters in this thesiscontribute novel proposals for non-differentiable neural network generation mechanisms, significant improvements to existing differentiable network generation mechanisms, and an assimilation of different learning paradigms in autogenerative networks.

No description available.

Master of Engineering

Gliomas are an aggressive class of brain tumors that are associated with a better prognosis at a lower grade level. Effective differentiation and classification are imperative for early treatment. MRI scans are a popular medical imaging modality to detect and diagnosis brain tumors due to its capability to non-invasively highlight the tumor region. With the rise of deep learning, researchers have used convolution neural networks for classification purposes in this domain, specifically pre-trained networks to reduce computational costs. However, with various MRI modalities, MRI machines, and poor image scan quality cause different network structures to have different performance metrics. Each pre-trained network is designed with a different structure that allows robust results given specific problem conditions. This thesis aims to cover the gap in the literature to compare the performance of popular pre-trained networks on a controlled dataset that is different than the network trained domain. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2020. / FAU Electronic Theses and Dissertations Collection

L’objectif de cette thèse est la réalisation expérimentale de calcul bio-inspiré en utilisant la dynamique transitoire d’oscillateurs magnétique nanométriques.Pour bien des tâches telle que la reconnaissance vocale, le cerveau fonctionne bien plus efficacement en terme d’énergie qu’un ordinateur classique. Le développement de puces neuro-inspirées offre donc la perspective de surmonter les limitations des processeurs actuels et de gagner plusieurs ordres de grandeurs sur la consommation énergétique du traitement de données. L’efficacité du cerveau à traiter des données est due à son architecture, qui est particulièrement adaptée à la reconnaissance de motifs. Les briques de base de cette architecture sont les neurones biologiques. Ceux-ci peuvent être vus comme des oscillateurs non linéaires qui interagissent et génèrent des cascades spatiales d’activations en réponse à une excitation. Cependant le cerveau comprend cent milliards de neurones et le développement d’une puce neuro-inspiré requerrait des oscillateurs de très petite dimension. Les oscillateurs à transfert de spin (STNO) sont de taille nanométrique, ont une réponse rapide (de l’ordre de la nanoseconde), sont fortement non-linéaires et leur réponse dépendante du couple de transfert de spin est aisément ajustable (par exemple par l’application d’un courant continu ou d’un champ magnétique). Ils fonctionnent à température ambiante, ont un très faible bruit thermique, et sont compatible avec les technologies CMOS. Ces caractéristiques en font d’excellents candidats pour la réalisation de réseaux artificiels de neurones compatibles avec un ordinateur classique.Dans cette thèse, nous avons utilisé un unique STNO pour générer le comportement d’un réseau de neurones. Ainsi l’oscillateur joue à tour de rôle chaque neurone. Une cascade temporelle remplace donc la cascade spatiale d’un réseau de neurones biologiques. En particulier nous avons utilisé la relaxation et la dépendance non-linéaire de l’amplitude des oscillations afin de réaliser du calcul neuromorphique. L’un des résultats principaux de cette thèse est la réalisation de reconnaissance vocale (reconnaissance de chiffres dits par 5 locuteurs différents) en obtenant un taux de reconnaissance à l’état de l’art de 99.6%. Nous avons pu montrer que les performances de la reconnaissance sont étroitement dépendantes des propriétés physiques du STNO tel que l’évolution de la largeur de raie, la puissance d’émission, ou la fréquence d’émission. Nous avons donc optimisé les conditions expérimentales (champs magnétiques et courant continu appliqués, fréquence du signal à traiter) afin de pouvoir utiliser au mieux les propriétés physiques du STNO pour la reconnaissance.  Les signaux vocaux requièrent d’être transformés du domaine temporel au domaine fréquentiel, avant de pouvoir être traités, et cette étape est réalisée numériquement en amont de l’expérience. Nous avons étudié l’influence de différents prétraitements sur la reconnaissance et mis en évidence le rôle majeur de la non-linéarité de ces derniers. Enfin, afin de pouvoir traiter des problèmes requérant de la mémoire, tel que par exemple des signaux sous forme de séquences temporelles, nous avons mesuré la mémoire que possède intrinsèquement un STNO, du fait de sa relaxation. Nous avons aussi augmenté cette mémoire à l’aide d’une boucle à retard. Ce dispositif a permis d’accroître la plage de mémoire de quelques centaines de nanosecondes à plus d’une dizaine de microsecondes. L’ajout de cette mémoire extrinsèque a permis de supprimer jusqu’à 99% des erreurs sur une tâche de reconnaissance de motifs temporels (reconnaissance de signaux sinusoïdaux et carrés). / This thesis studies experimentally the transient dynamics of magnetic nano-oscillators for brain-inspired computing.For pattern recognition tasks such as speech or visual recognition, the brain is much more energy efficient than classical computers. Developing brain-inspired chips opens the path to overcome the limitations of present processors and to win several orders of magnitude in the energy consumption of data processing. The efficiency of the brain originates from its architecture particularly well adapted for pattern recognition. The building blocks of this architecture are the biological neurons, which can be seen as interacting non-linear oscillators generating spatial chain reactions of activations. Nevertheless, the brain has one hundred billion neurons and a brain-inspired chip would require extremely small dimension oscillators. The spin-transfer torque oscillators (STNO) have nanometric size, they are fast (nanosecond time-scales), highly non-linear and their spin-torque dependent response is easily tunable (for instance by applying an external magnetic field or a d.c. current). They work at room temperature, they have a low thermal noise and they are compatible with CMOS technologies. Because of these features, they are excellent candidates for building hardware neural networks, which are compatible with the standard computers.In this thesis, we used a single STNO to emulate the behavior of a whole neural network. In this time multiplexed approach, the oscillator emulates sequentially each neuron and a temporal chain reaction replace the spatial chain reaction of a biological neural network. In particular, we used the relaxation and the non-linear dependence of the oscillation amplitude with the applied current to perform neuromorphic computing. One of the main results of this thesis is the demonstration of speech recognition (digits said by different speakers) with a state-of-the-art recognition rate of 99.6%. We show that the recognition performance is highly dependent on the physical properties of the STNO, such as the linewidth, the emission power or the frequency. We thus optimized the experimental bias conditions (external applied magnetic field, d.c. current and rate of the input) in order to leverage adequately the physical properties of the STNO for recognition. Voice waveforms require a time-to-frequency transformation before being processed, and this step is performed numerically before the experiment. We studied the influence of different time-to-frequency transformations on the final recognition rate, shading light on the critical role of their non-linear behavior. Finally, in order to solve problems requiring memory, such as temporal sequence analysis, we measured the intrinsic memory of a STNO, which comes from the relaxation of the oscillation amplitude. We also increased this memory, using a delayed feedback loop. This feedback improved the range of memory from a few hundreds of nanoseconds to more than ten microseconds. This feedback memory allows suppressing up to 99% of the errors on a temporal pattern recognition task (discrimination of sine and square waveforms).