In this research project, we introduced an agent-based banking system based on systematic trust. The features of the model and attributes of the agents are defined and analyzed precisely, and the results are explained. Some of this model's features include but are not limited to considering the savings system, insurance deposits, the impact of the Central Bank loans, and correlated regional shocks in a banking system. Different Scenarios are applied. The results indicate that by having the Central Bank loans in the model, the banking system experience dramatically fewer failures. Even if some correlated regional shocks occur, the system can be more stable than when the Central Bank does not exist. Moreover, the trust system establishes and forms during different financial periods based on the bank's clients’ point of view about the bank's performance as an intelligent system to attract more capital for the system by providing some information for the agents to join the more prestigious banks. Conclusively, in the early financial periods, banks need more financial supports to support the clients’ deposits and to make their reputation for attracting more clients; hence the Central Bank is an essential parameter to help the banks to be more stable and supports the banks in their early stages of growth. The Central Bank loans would be significantly important in panic times, such as regional correlated preference shocks. / Thesis / Master of Science (MSc)

Computer crime is a large problem (CSI, 2004; Kabay, 2001a; Kabay, 2001b). Security managers have a variety of tools at their disposal -- firewalls, Intrusion Detection Systems (IDSs), encryption, authentication, and other hardware and software solutions to combat computer crime. Many IDS variants exist which allow security managers and engineers to identify attack network packets primarily through the use of signature detection; i.e., the IDS recognizes attack packets due to their well-known "fingerprints" or signatures as those packets cross the network's gateway threshold. On the other hand, anomaly-based ID systems determine what is normal traffic within a network and reports abnormal traffic behavior. This paper will describe a methodology towards developing a more-robust Intrusion Detection System through the use of data-mining techniques and anomaly detection. These data-mining techniques will dynamically model what a normal network should look like and reduce the false positive and false negative alarm rates in the process. We will use classification-tree techniques to accurately predict probable attack sessions. Overall, our goal is to model network traffic into network sessions and identify those network sessions that have a high-probability of being an attack and can be labeled as a "suspect session." Subsequently, we will use these techniques inclusive of signature detection methods, as they will be used in concert with known signatures and patterns in order to present a better model for detection and protection of networks and systems.

Beginning the study with a regional network map with an intent to perform a detailed harmonic study for a certain location, the first question that comes up is how far out in the system should detailed modeling of individual devices (transmission lines, loads, transformers, capacitor banks, etc) be done. The reason why this is extremely important is because system components that will affect the frequency response characteristics in the specific location should not be missed or poorly modeled. Frequency scan is the simplest and most commonly used simulation technique used to characterize the response of a power system network as a function of frequency. Unfortunately, there are two major problems using frequency scan techniques when real harmonic studies are considered: 1) the size of the admittance matrices (this calculation is repeated using discrete frequency steps throughout the range of interest) may be so large that an exact mathematical model of the system is not realistic and 2) the complexity of a rigorous and complete mathematical model of the system does not necessarily explain the extent to which system components affect the frequency response characteristics in a specified location. It is seldom clear how much of the system must be represented in order to get accurate results in a harmonic study. Realistic procedures to identify whether to include a particular element in a detailed model or to lump the element into a simplifying equivalent are yet to be developed in the industry. It is safe to say that practicing engineers are using tools and techniques of questionable validity. Two new computer-oriented methods that use eigen analysis techniques to identify easily and accurately the boundary between system areas to be modeled in detail and those represented by equivalents are proposed in this dissertation. The key here is to recognize that not all elements present in the ?external? system will participate in the resonant harmonic modes and could therefore be lumped into a simplified short-circuit equivalent. Achieving these objectives from either one of the two methods can be economically attractive. In short, the work described in this dissertation is a fundamentally sound alternative for the purposes of network equivalencing and model reduction.

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<p>This work describes an architecture which enables experiments in optimization of networks that represent systems in diverse application domains, e.g. multi-product food production plants, gasoline blending and shipment, heat exchanger networks in refineries, etc. The prototype implementation is a web-based workbench (NOPT). Design of the workbench enables instantiation of different application domains via attributes describing entities (materials, energy) flowing through network arcs, and via node models relevant to the domain. From data describing the network attributes, NOPT generates a mathematical model described by a set of linear equations and provides a user with abilities to select appropriate solution algorithms. Multi-step composite algorithms, each solving a subnetwork or an entire network for specific time periods can be constructed with input from the user. Some of the steps in the algorithm can be non-linear procedures which compute specific model parameters. Hence, the architecture enables solution of bi linear systems of type “x*y” (e.g. energy balances) by first solving for “x’ (e.g. mass flows) from some other set of equations (e.g. mass balances) and then solve for “y” since “x’ is known. Current architecture of NOPT also supports the inclusion of external node models that helps user to import his customized node models into the workbench via the feature called User Node.</p> / Master of Computer Science (MCS)

Systems biology comprises the global, integrated analysis of large-scale data encoding different levels of biological information with the aim to obtain global insight into the cellular networks. Several studies have unveiled the modular and hierarchical organization inherent in these networks. In this dissertation, we propose and develop innovative systems approaches to integrate multi-source biological data in a modular manner for network inference and biomarker discovery in complex diseases such as breast cancer. The first part of the dissertation is focused on gene module identification in gene expression data. As the most popular way to identify gene modules, many cluster algorithms have been applied to the gene expression data analysis. For the purpose of evaluating clustering algorithms from a biological point of view, we propose a figure of merit based on Kullback-Leibler divergence between cluster membership and known gene ontology attributes. Several benchmark expression-based gene clustering algorithms are compared using the proposed method with different parameter settings. Applications to diverse public time course gene expression data demonstrated that fuzzy c-means clustering is superior to other clustering methods with regard to the enrichment of clusters for biological functions. These results contribute to the evaluation of clustering outcomes and the estimations of optimal clustering partitions. The second part of the dissertation presents a hybrid computational intelligence method to infer gene regulatory modules. We explore the combined advantages of the nonlinear and dynamic properties of neural networks, and the global search capabilities of the hybrid genetic algorithm and particle swarm optimization method to infer network interactions at modular level. The proposed computational framework is tested in two biological processes: yeast cell cycle, and human Hela cancer cell cycle. The identified gene regulatory modules were evaluated using several validation strategies: 1) gene set enrichment analysis to evaluate the gene modules derived from clustering results; (2) binding site enrichment analysis to determine enrichment of the gene modules for the cognate binding sites of their predicted transcription factors; (3) comparison with previously reported results in the literatures to confirm the inferred regulations. The proposed framework could be beneficial to biologists for predicting the components of gene regulatory modules in which any candidate gene is involved. Such predictions can then be used to design a more streamlined experimental approach for biological validation. Understanding the dynamics of these gene regulatory modules will shed light on the related regulatory processes. Driven by the fact that complex diseases such as cancer are “diseases of pathways”, we extended the module concept to biomarker discovery in cancer research. In the third part of the dissertation, we explore the combined advantages of molecular interaction network and gene expression profiles to identify biomarkers in cancer research. The reliability of conventional gene biomarkers has been challenged because of the biological heterogeneity and noise within and across patients. In this dissertation, we present a module-based biomarker discovery approach that integrates interaction network topology and high-throughput gene expression data to identify markers not as individual genes but as modules. To select reliable biomarker sets across different studies, a hybrid method combining group feature selection with ensemble feature selection is proposed. First, a group feature selection method is used to extract the modules (subnetworks) with discriminative power between disease groups. Then, an ensemble feature selection method is used to select the optimal biomarker sets, in which a double-validation strategy is applied. The ensemble method allows combining features selected from multiple classifications with various data subsampling to increase the reliability and classification accuracy of the final selected biomarker set. The results from four breast cancer studies demonstrated the superiority of the module biomarkers identified by the proposed approach: they can achieve higher accuracies, and are more reliable in datasets with same clinical design. Based on the experimental results above, we believe that the proposed systems approaches provide meaningful solutions to discover the cellular regulatory processes and improve the understanding about disease mechanisms. These computational approaches are primarily developed for analysis of high-throughput genomic data. Nevertheless, the proposed methods can also be extended to analyze high-throughput data in proteomics and metablomics areas. / Ph. D.

Thesis (MSc)--Stellenbosch University, 2011 / ENGLISH ABSTRACT: With billions of current internet users interacting through social networks, the need has arisen to analyze the structure of these networks. Many authors have proposed random graph models for social networks in an attempt to understand and reproduce the dynamics that govern social network development. This thesis proposes a random graph model that generates social networks using a community-based approach, in which users’ affiliations to communities are explicitly modeled and then translated into a social network. Our approach explicitly models the tendency of communities to overlap, and also proposes a method for determining the probability of two users being connected based on their levels of commitment to the communities they both belong to. Previous community-based models do not incorporate community overlap, and assume mutual members of any community are automatically connected. We provide a method for fitting our model to real-world social networks and demonstrate the effectiveness of our approach in reproducing real-world social network characteristics by investigating its fit on two data sets of current online social networks. The results verify that our proposed model is promising: it is the first community-based model that can accurately reproduce a variety of important social network characteristics, namely average separation, clustering, degree distribution, transitivity and network densification, simultaneously. / AFRIKAANSE OPSOMMING: Met biljoene huidige internet-gebruikers wat deesdae met behulp van aanlyn sosiale netwerke kommunikeer, het die analise van hierdie netwerke in die navorsingsgemeenskap toegeneem. Navorsers het al verskeie toevalsgrafiekmodelle vir sosiale netwerke voorgestel in ’n poging om die dinamika van die ontwikkeling van dié netwerke beter te verstaan en te dupliseer. In hierdie tesis word ’n nuwe toevalsgrafiekmodel vir sosiale netwerke voorgestel wat ’n gemeenskapsgebaseerde benadering volg, deurdat gebruikers se verbintenisse aan gemeenskappe eksplisiet gemodelleer word, en dié gemeenskapsmodel dan in ’n sosiale netwerk omskep word. Ons metode modelleer uitdruklik die geneigdheid van gemeenskappe om te oorvleuel, en verskaf ’n metode waardeur die waarskynlikheid van vriendskap tussen twee gebruikers bepaal kan word, op grond van hulle toewyding aan hulle wedersydse gemeenskappe. Vorige modelle inkorporeer nie gemeenskapsoorvleueling nie, en aanvaar ook dat alle lede van dieselfde gemeenskap vriende sal wees. Ons verskaf ’n metode om ons model se parameters te pas op sosiale netwerk datastelle en vertoon die vermoë van ons model om eienskappe van sosiale netwerke te dupliseer. Die resultate van ons model lyk belowend: dit is die eerste gemeenskapsgebaseerde model wat gelyktydig ’n belangrike verskeidenheid van sosiale netwerk eienskappe, naamlik gemiddelde skeidingsafstand, samedromming, graadverdeling, transitiwiteit en netwerksverdigting, akkuraat kan weerspieël.

In this thesis, three methods are presented to model the decision process of whether or not to bid for a tender in offshore petroleum platform fabrication. A sample data and the assessment based on this data are gathered from an offshore petroleum platform fabrication company and this information is analyzed to understand the significant parameters in the industry. The alternative methods, &ldquo / Regression Analysis&rdquo / , &ldquo / Neural Network Method&rdquo / and &ldquo / Fuzzy Neural Network Method&rdquo / , are used for modeling of the bidding decision process. The regression analysis examines the data statistically where the neural network method and fuzzy neural network method are based on artificial intelligence. The models are developed using the bidding data compiled from the offshore petroleum platform fabrication projects. In order to compare the prediction performance of these methods &ldquo / Cross Validation Method&rdquo / is utilized. The models developed in this study are compared with the bidding decision method used by the company. The results of the analyses show that regression analysis and neural network method manage to have a prediction performance of 80% and fuzzy neural network has a prediction performance of 77,5% whereas the method used by the company has a prediction performance of 47,5%. The results reveal that the suggested models achieve significant improvement over the existing method for making the correct bidding decision.

The frequent and large-scale network attacks have led to an increased need for developing techniques for analyzing network traffic. If efficient analysis tools were available, it could become possible to detect the attacks, anomalies and to appropriately take action to contain the attacks before they have had time to propagate across the network. In this dissertation, we suggest a technique for traffic anomaly detection based on analyzing the correlation of destination IP addresses and distribution of image-based signal in postmortem and real-time, by passively monitoring packet headers of traffic. This address correlation data are transformed using discrete wavelet transform for effective detection of anomalies through statistical analysis. Results from trace-driven evaluation suggest that the proposed approach could provide an effective means of detecting anomalies close to the source. We present a multidimensional indicator using the correlation of port numbers as a means of detecting anomalies. We also present a network measurement approach that can simultaneously detect, identify and visualize attacks and anomalous traffic in real-time. We propose to represent samples of network packet header data as frames or images. With such a formulation, a series of samples can be seen as a sequence of frames or video. Thisenables techniques from image processing and video compression such as DCT to be applied to the packet header data to reveal interesting properties of traffic. We show that ??scene change analysis?? can reveal sudden changes in traffic behavior or anomalies. We show that ??motion prediction?? techniques can be employed to understand the patterns of some of the attacks. We show that it may be feasible to represent multiple pieces of data as different colors of an image enabling a uniform treatment of multidimensional packet header data. Measurement-based techniques for analyzing network traffic treat traffic volume and traffic header data as signals or images in order to make the analysis feasible. In this dissertation, we propose an approach based on the classical Neyman-Pearson Test employed in signal detection theory to evaluate these different strategies. We use both of analytical models and trace-driven experiments for comparing the performance of different strategies. Our evaluations on real traces reveal differences in the effectiveness of different traffic header data as potential signals for traffic analysis in terms of their detection rates and false alarm rates. Our results show that address distributions and number of flows are better signals than traffic volume for anomaly detection. Our results also show that sometimes statistical techniques can be more effective than the NP-test when the attack patterns change over time.