Network Analysis with Stochastic Grammars

Digital forensics requires significant manual effort to identify items of evidentiary interest from the ever-increasing volume of data in modern computing systems. One of the tasks digital forensic examiners conduct is mentally extracting and constructing insights from unstructured sequences of events. This research assists examiners with the association and individualization analysis processes that make up this task with the development of a Stochastic Context -Free Grammars (SCFG) knowledge representation for digital forensics analysis of computer network traffic. SCFG is leveraged to provide context to the low-level data collected as evidence and to build behavior profiles. Upon discovering patterns, the analyst can begin the association or individualization process to answer criminal investigative questions. Three contributions resulted from this research. First , domain characteristics suitable for SCFG representation were identified and a step -by- step approach to adapt SCFG to novel domains was developed. Second, a novel iterative graph-based method of identifying similarities in context-free grammars was developed to compare behavior patterns represented as grammars. Finally, the SCFG capabilities were demonstrated in performing association and individualization in reducing the suspect pool and reducing the volume of evidence to examine in a computer network traffic analysis use case

Gambling risk perception and decision making

Cognitive and biopsychological research has identified a significant relationship between perception, decision making and the negative consequences associated with sustained gambling. Drug and alcohol research suggests that how individuals navigate decisions involving motivating but risky activities involves several important, distinct, but interrelated aspects of cognition. Nevertheless, risk perception and decision making has received little attention in the gambling literature. The aim of the current thesis therefore was to investigate risk perception in gambling, and to develop a model of gambling decision making mindful of risk perception concepts. The project applied the Mental Models methodology and included: a literature review, a qualitative study evaluating expert opinions regarding gambling risk decision making, a second qualitative study evaluating lay gambler mental models of risk, and a quantitative evaluation of risk perception and decision making concepts via a self-report questionnaire. Data from all phases of the project were used to develop an assessment tool (Gambling Risk Decisions Questionnaire) and theoretical model of gambling risk decision making. It was anticipated that understanding the processes by which risk perception predisposes an individual to maintain gambling despite adverse consequences would act as an invaluable guide for preventative educational campaigns, clinical treatment, and social policy interventions. Taken together, results of the four studies confirmed the importance of relationships between decision making, behaviour, consequences, and disorder, with disorder largely predictable based on several core decision making factors, despite individual variation in clinical presentation

The Utah Statesman, April 20, 1984

Weekly student newspaper of Utah State University in Logan.https://digitalcommons.usu.edu/newspapers/2546/thumbnail.jp