Detecting fraud in cellular telephone networks

Thesis (MSc)--University of Stellenbosch, 2005.ENGLISH ABSTRACT: Cellular network operators globally loose between 3% and 5% of their annual revenue to telecommunications fraud. Hence it is of great importance that fraud management systems are implemented to detect, alarm, and shut down fraud within minutes, minimising revenue loss. Modern proprietary fraud management systems employ (i) classification methods, most often artificial neural networks learning from classified call data records to classify new call data records as fraudulent or legitimate, (ii) statistical methods building subscriber behaviour profiles based on the subscriber’s usage in the cellular network and detecting sudden changes in behaviour, and (iii) rules and threshold values defined by fraud analysts, utilising their knowledge of valid fraud cases and the false alarm rate as guidance. The purpose of this thesis is to establish a context for and evaluate the performance of well-known data mining techniques that may be incorporated in the fraud detection process. Firstly, a theoretical background of various well-known data mining techniques is provided and a number of seminal articles on fraud detection, which influenced this thesis, are summarised. The cellular telecommunications industry is introduced, including a brief discussion of the types of fraud experienced by South African cellular network operators. Secondly, the data collection process and the characteristics of the collected data are discussed. Different data mining techniques are applied to the collected data, demonstrating how user behaviour profiles may be built and how fraud may be predicted. An appraisal of the performances and appropriateness of the different data mining techniques is given in the context of the fraud detection process. Finally, an indication of further work is provided in the conclusion to this thesis, in the form of a number of recommendations for possible adaptations of the fraud detection methods, and improvements thereof. A combination of data mining techniques that may be used to build a comprehensive fraud detection model is also suggested.AFRIKAANSE OPSOMMING: Sellulêre netwerk operateurs verloor wêreldwyd tussen 3% en 5% van hul jaarlikse inkomste as gevolg van telekommunikasie bedrog. Dit is dus van die uiterse belang dat bedrog bestuurstelsels geïmplimenteer word om bedrog op te spoor, alarms te genereer, en bedrog binne minute te staak om verlies aan inkomste tot ’n minimum te beperk. Moderne gepatenteerde bedrog bestuurstelsels maak gebruik van (i) klassifikasie metodes, mees dikwels kunsmatige neurale netwerke wat leer vanaf geklassifiseerde oproep rekords en gebruik word om nuwe oproep rekords as bedrog-draend of nie bedrog-draend te klassifiseer, (ii) statistiese metodes wat gedragsprofiele van ’n intekenaar bou, gebaseer op die intekenaar se gedrag in die sellulêre netwerk, en skielike verandering in gedrag opspoor, en (iii) reëls en drempelwaardes wat deur bedrog analiste daar gestel word, deur gebruik te maak van hulle ondervinding met geldige gevalle van bedrog en die koers waarteen vals alarms gegenereer word. Die doel van hierdie tesis is om ’n konteks te bepaal vir en die werksverrigting te evalueer van bekende data ontginningstegnieke wat in bedrog opsporingstelsels gebruik kan word. Eerstens word ’n teoretiese agtergrond vir ’n aantal bekende data ontginningstegnieke voorsien en ’n aantal gedagteryke artikels wat oor bedrog opsporing handel en wat hierdie tesis beïnvloed het, opgesom. Die sellulêre telekommunikasie industrie word bekend gestel, insluitend ’n kort bespreking oor die tipes bedrog wat deur Suid-Afrikaanse sellulˆere telekommunikasie netwerk operateurs ondervind word. Tweedens word die data versamelingsproses en die eienskappe van die versamelde data bespreek. Verskillende data ontginningstegnieke word vervolgens toegepas op die versamelde data om te demonstreer hoe gedragsprofiele van gebruikers gebou kan word en hoe bedrog voorspel kan word. Die werksverrigting en gepastheid van die verskillende data ontginningstegnieke word bespreek in die konteks van die bedrog opsporingsproses. Laastens word ’n aanduiding van verdere werk in die gevolgtrekking tot hierdie tesis verskaf, en wel in die vorm van ’n aantal aanbevelings oor moontlike aanpassings en verbeterings van die bedrog opsporingsmetodes wat beskou en toegepas is. ’n Omvattende bedrog opsporingsmodel wat gebruik maak van ’n kombinasie van data ontginningstegnieke word ook voorgestel

An Improved ATP FRET Sensor for Yeast Shows Heterogeneity during Nutrient Transitions

Adenosine 5-triphosphate (ATP) is the main free energy carrier in metabolism. In budding yeast, shifts to glucose-rich conditions cause dynamic changes in ATP levels, but it is unclear how heterogeneous these dynamics are at a single-cell level. Furthermore, pH also changes and affects readout of fluorescence-based biosensors for single-cell measurements. To measure ATP changes reliably in single yeast cells, we developed yAT1.03, an adapted version of the AT1.03 ATP biosensor, that is pH-insensitive. We show that pregrowth conditions largely affect ATP dynamics during transitions. Moreover, single-cell analyses showed a large variety in ATP responses, which implies large differences of glycolytic startup between individual cells. We found three clusters of dynamic responses, and we show that a small subpopulation of wild-type cells reached an imbalanced state during glycolytic startup, characterized by low ATP levels. These results confirm the need for new tools to study dynamic responses of individual cells in dynamic environments

Biphasic Cell-Size and Growth-Rate Homeostasis by Single Bacillus subtilis Cells

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Taking chances and making mistakes:non-genetic phenotypic heterogeneity and its consequences for surviving in dynamic environments

Natural selection has shaped the strategies for survival and growth of microorganisms. The success of microorganisms depends not only on slow evolutionary tuning but also on the ability to adapt to unpredictable changes in their environment. In principle, adaptive strategies range from purely deterministic mechanisms to those that exploit the randomness intrinsic to many cellular and molecular processes. Depending on the environment and selective pressures, particular strategies can lie somewhere along this continuum. In recent years, non-genetic cell-to-cell differences have received a lot of attention, not least because of their potential impact on the ability of microbial populations to survive in dynamic environments. Using several examples, we describe the origins of spontaneous and induced mechanisms of phenotypic adaptation. We identify some of the commonalities of these examples and consider the potential role of chance and constraints in microbial phenotypic adaptation

Parallel changes in gene expression in peripheral blood mononuclear cells and the brain after maternal separation in the mouse

Abstract Background The functional integration of the neuro-, endocrine- and immune-systems suggests that the transcriptome of white blood cells may reflect neuropsychiatric states, and be used as a non-invasive diagnostic indicator. We used a mouse maternal separation model, a paradigm of early adversity, to test the hypothesis that transcriptional changes in peripheral blood mononuclear cells (PBMCs) are paralleled by specific gene expression changes in prefrontal cortex (PFC), hippocampus (Hic) and hypothalamus (Hyp). Furthermore, we evaluated whether gene expression profiles of PBMCs could be used to predict the separation status of individual animals. Findings Microarray gene expression profiles of all three brain regions provided substantial evidence of stress-related neural differences between maternally separated and control animals. For example, changes in expression of genes involved in the glutamatergic and GABAergic systems were identified in the PFC and Hic, supporting a stress-related hyperglutamatergic state within the separated group. The expression of 50 genes selected from the PBMC microarray data provided sufficient information to predict treatment classes with 95% accuracy. Importantly, stress-related transcriptome differences in PBMC populations were paralleled by stress-related gene expression changes in CNS target tissues. Conclusion These results confirm that the transcriptional profiles of peripheral immune tissues occur in parallel to changes in the brain and contain sufficient information for the efficient diagnostic prediction of stress-related neural states in mice. Future studies will need to evaluate the relevance of the predictor set of 50 genes within clinical settings, specifically within a context of stress-related disorders.</p

Metabolism at Evolutionary Optimal States

Metabolism is generally required for cellular maintenance and for the generation of offspring under conditions that support growth. The rates, yields (efficiencies), adaptation time and robustness of metabolism are therefore key determinants of cellular fitness. For biotechnological applications and our understanding of the evolution of metabolism, it is necessary to figure out how the functional system properties of metabolism can be optimized, via adjustments of the kinetics and expression of enzymes, and by rewiring metabolism. The trade-offs that can occur during such optimizations then indicate fundamental limits to evolutionary innovations and bioengineering. In this paper, we review several theoretical and experimental findings about mechanisms for metabolic optimization

A yeast FRET biosensor enlightens cAMP signaling

The cAMP-PKA signaling cascade in budding yeast regulates adaptation to changing environments. We developed yEPAC, a FRET-based biosensor for cAMP measurements in yeast. We used this sensor with flow cytometry for high-throughput single cell-level quantification during dynamic changes in response to sudden nutrient transitions. We found that the characteristic cAMP peak differentiates between different carbon source transitions and is rather homogenous among single cells, especially for transitions to glucose. The peaks are mediated by a combination of extracellular sensing and intracellular metabolism. Moreover, the cAMP peak follows the Weber-Fechner law; its height scales with the relative, and not the absolute, change in glucose. Last, our results suggest that the cAMP peak height conveys information about prospective growth rates. In conclusion, our yEPAC-sensor makes possible new avenues for understanding yeast physiology, signaling, and metabolic adaptation

Statistics and simulation of growth of single bacterial cells: Illustrations with B. subtilis and E. coli

The inherent stochasticity of molecular reactions prevents us from predicting the exact state of single-cells in a population. However, when a population grows at steady-state, the probability to observe a cell with particular combinations of properties is fixed. Here we validate and exploit existing theory on the statistics of single-cell growth in order to predict the probability of phenotypic characteristics such as cell-cycle times, volumes, accuracy of division and cell-age distributions, using real-time imaging data for Bacillus subtilis and Escherichia coli. Our results show that single-cell growth-statistics can accurately be predicted from a few basic measurements. These equations relate different phenotypic characteristics, and can therefore be used in consistency tests of experimental single-cell growth data and prediction of single-cell statistics. We also exploit these statistical relations in the development of a fast stochastic-simulation algorithm of single-cell growth and protein expression. This algorithm greatly reduces computational burden, by recovering the statistics of growing cell-populations from the simulation of only one of its lineages. Our approach is validated by comparison of simulations and experimental data. This work illustrates a methodology for the prediction, analysis and tests of consistency of single-cell growth and protein expression data from a few basic statistical principles.BN/Marileen Dogterom La