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This study aims to find how crime rates in Maryland are connected to different socioeconomic elements. This study is focused on understanding how crime rates link up with factors like unemployment, household income levels, racial backgrounds, and the level of education people have. A quantitative analysis of state-level crime data from 2010 to 2020 was used; the study employs a comprehensive range of methods. Conducted a detailed picture through descriptive analysis, then reshaped data using log and square root transformations, and tested hypotheses with a t-test. The paper further examines the relationships between variables through a correlation matrix before applying ordinary least squares regression to predict outcomes. It has been discovered that areas facing more significant economic challenges seen through higher Unemployment rates and diminished earnings frequently show an uptick in crime. The examination highlights a strong link between unemployment and criminal behavior, especially in counties where families consistently earn less. Research highlights how vital education is by showing that when people achieve higher levels of education, they tend to commit fewer crimes. The study illuminates the intricate interplay between economic factors and criminal activities, offering invaluable insights for law enforcement agencies and policymakers. These insights can guide the development of effective strategies to combat crime; ultimately, the research deepens our understanding of how economic fluctuations significantly influence crime rates in Maryland. 

Cyber threat intelligence (CTI) is an actionable information or insight an organization uses to understand potential vulnerabilities it does have and threats it is facing. One important CTI for proactive cyber defense is exploit type with possible values system, web, network, website or Mobile. This study compares the performance of machine learning algorithms in predicating exploit types using form posts in the dark web, which is a semi- structured dataset collected from dark web. The study uses the CRISP data science approach. The results of the study show that machine learning algorithms which are function-based including support vector machine and deep-learning using artificial neural network are more accurate than those algorithms which are based on tree including Random Forest and Decision-Tree for CTI discovery from semi-structured dataset. Future research will include the use of high-performance computing and advanced deep-learning algorithms. 

Over the last three decades, the growth in housing costs relative to household incomes across cities in the United States has dramatically affected households' housing options. For this study, we apply a logit model to data from the American Housing Survey to provide evidence on how rising house costs affect female-headed households' decisions to move from the current home to another. Estimates reveal that total housing cost is a significant determinant of a female-headed household’s decision to move. We also found that lower-income female-headed households are more likely to move to a new location than higher-income female-headed households. These results support the idea that affordable housing programs should be maintained and expanded to offer some alleviation to the burden of rising housing costs on lower-income female-headed households and other vulnerable groups