Introduction Although cardiovascular diseases (CVD) are the leading cause of death among Americans, significant disparities persist in CVD prevalence, morbidity, and mortality based on race and ethnicity. However, few studies have examined risk factor reduction among the poor and ethnic minorities. Methods Community-based participatory research (CBPR) study using a cluster randomized design — African-American church congregations are the units of randomization and individuals within the congregations are the units of analysis. Outcome variables include dietary change (Diet History Questionnaire), level of physical activity (7-Day Physical Activity Recall), lipoprotein levels, blood pressure, fasting glucose, and hemoglobin A1c. Results Eighteen (18) church congregations were randomized to either a health maintenance intervention or a control condition. Complete data were obtained on 392 African-American individuals, 18 to 70 years of age, predominantly employed women with more than a high school diploma. Treatment and intervention groups were similar at baseline on saturated fat intake, metabolic equivalent of tasks (METS) per day, and other risk factors for CVD. Conclusions The GoodNEWS trial successfully recruited and evaluated CVD-related risk among African-American participants using a CBPR approach. Several logistical challenges resulted in extending the recruitment, preliminary training, and measurement periods. The challenges were overcome with the assistance of a local community consultant and a professional event planner. Our experience supports the need for incorporating non-traditional community-based staff into the design and operational plan of CBPR trials.

Attack graphs have been used to model the vulnerabilities of the systems and their potential exploits. The successful exploits leading to the partial/total failure of the systems are subject of keen security interest. Considerable effort has been expended in exhaustive modeling, analyses, detection, and mitigation of attacks. One prominent methodology involves constructing attack graphs of the pertinent system for analysis and response strategies. This not only gives the simplified representation of the system, but also allows prioritizing the security properties whose violations are of greater concern, for both detection and repair. We present a survey and critical study of state-of-the-art technologies in attack graph generation and use in security system. Based on our research, we identify the potential, challenges, and direction of the current research in using attack graphs.

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Game theory has been researched extensively in network security demonstrating an advantage of modeling the interactions between attackers and defenders. Game theoretic defense solutions have continuously evolved in most recent years. One of the pressing issues in composing a game theoretic defense system is the development of consistent quantifiable metrics to select the best game theoretic defense model. We survey existing game theoretic defense, information assurance, and risk assessment frameworks that provide metrics for information and network security and performance assessment. Coupling these frameworks, we propose a game theoretic approach to attack-defense and performance metric taxonomy (ADAPT). ADAPT uses three classifications of metrics: (i) Attacker, (ii) Defender (iii) Performance. We proffer ADAPT with an attempt to aid game theoretic performance metrics. We further propose a game decision system (GDS) that uses ADAPT to compare competing game models. We demonstrate our approach using a distributed denial of service (DDoS) attack scenario.

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