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Honeypots are designed to trap the attacker with the purpose of investigating its malicious behavior. Owing to the increasing variety and sophistication of cyber attacks, how to capture high-quality attack data has become a challenge in the context of honeypot area. All-round honeypots, which mean significant improvement in sensibility, countermeasure and stealth, are necessary to tackle the problem. In this paper, we propose a novel honeypot architecture termed HoneyDOC to support all-round honeypot design and implementation. Our HoneyDOC architecture clearly identifies three essential independent and collaborative modules, Decoy, Captor and Orchestrator. Based on the efficient architecture, a Software-Defined Networking (SDN) enabled honeypot system is designed, which supplies high programmability for technically sustaining the features for capturing high-quality data. A proof-of-concept system is implemented to validate its feasibility and effectiveness. The experimental results show the benefits by using the proposed architecture comparing to the previous honeypot solutions., Comment: None

Cardiovascular disease (CVD) stands as the predominant cause of mortality on a global scale, with diabetes mellitus (DM) emerging as a prominent risk factor for CVD. Notably, adults afflicted by DM face a nearly twofold increased risk of CVD-related mortality compared to their non-DM counterparts. The present dissertation undertakes the task of formulating pre- ventive strategies for CVD within the DM patient population. The first chapter of this study endeavors to ascertain the residual risk of CVD stemming from hypertriglyceridemia (HTG) in the DM population, drawing data from the National Health Nutrition and Examination Survey (NHANES) spanning the years 2007 to 2014. Interestingly, it is observed that despite the implementation of first-line lipid-lowering therapy involving statin usage and optimal low- density lipoprotein cholesterol (LDL-C) control, approximately one-fifth of this population continues to exhibit HTG. Additionally, three-quarters of DM patients possess a moderate to high 10-year risk for CVD. The second chapter aims to assess the potential impact of two novel DM medications, namely liraglutide (glucagon-like peptide-1 receptor agonists (GLP-1 RAs)) (Chapter 2) and empagliflozin (sodium-glucose cotransporter 2 inhibitor (SGLT-2i)) (Chapter 3), on CVD prevention. The evaluation is conducted in terms of the preventive capacity for CVD events while on treatment, utilizing the NAHNES database. Followingix the meticulous application of key inclusion and exclusion criteria derived from corresponding clinical trials, an estimated 4.2 million and 1.86 million US DM patients are deemed eligi- ble for these treatments, respectively. Moreover, approximately 21,209 and 12,066 major cardiovascular events, comprising a composite outcome of cardiovascular death, myocardial infarction, and stroke, could be averted annually, respectively. The forth chapter endeav- ors to explore the impact of multifactorial risk control—encompassing body mass index, blood gluco

Chen Wei, Wenjun Fan, Ying Zhang, Yixiang Liu, Zhenjiang Ding, Yueqiao Si, Jingyi Liu, Lixian Sun Department of Cardiology, The Affiliated Hospital of Chengde Medical University, Chengde, Hebei, 067000, People’s Republic of ChinaCorrespondence: Lixian Sun, Department of Cardiology, The Affiliated Hospital of Chengde Medical University, Chengde, Hebei, 067000, People’s Republic of China, Tel +86 0314 227 9016, Fax +86 0314 227 4895, Email lixiansun01@126.comPurpose: To develop and validate two nomograms incorporating the albumin/neutrophil-to-lymphocyte ratio score (ANS) for predicting the risk of coronary artery disease (CAD) or subclinical CAD.Patients and Methods: Four hundred fifty patients with suspected CAD who underwent coronary computed tomographic angiography were consecutively enrolled between September 2015 and June 2017. Nomograms were established based on independent predictors of CAD or subclinical CAD.Results: In total, 437 patients with suspected CAD who underwent coronary computed tomographic angiography were included. Male sex, age ≥ 65 years, smoking, hypertension, diabetes, dyslipidemia, ischemic stroke, and ANS were independent predictors of CAD and subclinical CAD. The areas under the curve of each nomogram were 0.799 (95% CI: 0.752– 0.846) and 0.809 (95% CI: 0.762– 0.856), respectively. The calibration curve and decision curve analysis showed good performance for the diagnostic nomograms. The prediction of CAD or subclinical CAD by the ANS was not modified by the independent predictors (all, p for interaction > 0.05).Conclusion: Our ANS-based nomograms can provide accurate and individualized risk predictions for patients with suspected CAD or subclinical CAD.Keywords: albumin/neutrophil-to-lymphocyte ratio score, ANS, coronary artery disease, diagnosis, nomogram

Xinchen Wang,1 Chen Wei,1 Wenjun Fan,1 Lixian Sun,1 Ying Zhang,1 Qiyu Sun,2 Yixiang Liu,1 Jingyi Liu1 1Department of Cardiology, The Affiliated Hospital of Chengde Medical University, The Chengde Institute of Cardiovascular Diseases, Chengde, Hebei, 067000, People’s Republic of China; 2Department of Clinical Laboratory, The Affiliated Hospital of Chengde Medical University, Chengde, Hebei, 067000, People’s Republic of ChinaCorrespondence: Lixian Sun, Department of Cardiology, The Affiliated Hospital of Chengde Medical University, Chengde, Hebei, 067000, People’s Republic of China, Tel +86 314 227 9016, Fax +86 314 227 4895, Email lixiansun01@126.comPurpose: The decreased advanced lung cancer inflammation index (ALI), defined as body mass index (BMI) &ast; albumin (Alb)/neutrophil-to-lymphocyte ratio (NLR), is an independent prognostic risk factor for overall survival in gastric, lung, and colorectal cancers. This study aimed to investigate the value of ALI in predicting the risk of major adverse cardiovascular events (MACEs) in patients with acute coronary syndrome (ACS).Patients and Methods: A total of 1624 patients with ACS undergoing percutaneous coronary intervention (PCI) were consecutively enrolled between January 2016 and December 2018. Follow-up data were collected at 1, 3, 6, and 12 months and annually thereafter. The primary endpoints were MACEs. All endpoints were defined as all-cause mortality, recurrent angina pectoris, restenosis/intra stent thrombosis, stroke, heart failure, and all-cause bleeding.Results: The MACEs group and non-MACEs group showed significant differences in patients with age > 65 years (28 [50.0%] vs 319 [23.7%]), history of heart failure (16 [28.6%] vs 127 [9.4%]), history of ischemic stroke (14 [25.0%] vs 186 [13.8%]), history of cardiogenic shock (6 [10.71%] vs 16 [1.19%]), left ventricular ejection fraction < 40% (8 [14.29%] vs 33 [2.46%]), and ALI < 343.96 (44 [78.65%] vs 680 [50.60%]) (all p< 0.001). The o

PURPOSE: South Asian (SA) persons have increased risks for diabetes mellitus (DM) and atherosclerotic cardiovascular disease (ASCVD). We examined whether the association of DM with subclinical atherosclerosis assessed by coronary artery calcium (CAC) differs in SA versus other ethnic groups. METHODS: We studied adults from the Multi-Ethnic Study of Atherosclerosis and the Mediators of Atherosclerosis in South Asians Living in America studies without ASCVD. CAC was examined among those normoglycemic, pre-DM and DM. Logistic regression examined pre-DM and DM with the odds of any CAC > 0 and CAC ≥ 100. RESULTS: Among 7562 participants, CAC > 0 and CAC ≥ 100 in those with DM was highest in non-Hispanic White (NHW) (80% and 48%) and SA (72% and 41%) persons. Adjusted Ln (CAC + 1) was highest in NHW (3.68 ± 0.21) and SA (3.60 ± 0.23) (p < .01) DM patients. SA and NHW adults with DM (vs normoglycemic) had highest odds of CAC > 0 (2.13 and 2.27, respectively, p < .01). For CAC ≥ 100, SA and Chinese adults had the highest odds (2.28 and 2.27, respectively, p < .01). Fasting glucose and glycated hemoglobin were most strongly associated with CAC among SA. CONCLUSIONS: Diabetes mellitus most strongly relates to any CAC in SA and NHW adults and CAC ≥ 100 in SA and Chinese adults, helping to explain the relation of DM with ASCVD in these populations.

BACKGROUND: The PI3K/AKT pathway transduces the majority of the metabolic actions of insulin. In addition to cytosolic targets, insulin-stimulated phospho-AKT also translocates to mitochondria in the myocardium. Mouse models of diabetes exhibit impaired mitochondrial AKT signaling but the implications of this on cardiac structure and function is unknown. We hypothesized that loss of mitochondrial AKT signaling is a critical step in cardiomyopathy and reduces cardiac oxidative phosphorylation. METHODS: To focus our investigation on the pathophysiological consequences of this mitochondrial signaling pathway, we generated transgenic mouse models of cardiac-specific, mitochondria-targeting, dominant negative AKT1 (CAMDAKT) and constitutively active AKT1 expression (CAMCAKT). Myocardial structure and function were examined using echocardiography, histology, and biochemical assays. We further investigated the underlying effects of mitochondrial AKT1 on mitochondrial structure and function, its interaction with ATP synthase, and explored in vivo metabolism beyond the heart. RESULTS: Upon induction of dominant negative mitochondrial AKT1, CAMDAKT mice developed cardiac fibrosis accompanied by left ventricular hypertrophy and dysfunction. Cardiac mitochondrial oxidative phosphorylation efficiency and ATP content were reduced, mitochondrial cristae structure was lost, and ATP synthase structure was compromised. Conversely, CAMCAKT mice were protected against development of diabetic cardiomyopathy when challenged with a high calorie diet. Activation of mitochondrial AKT1 protected cardiac function and increased fatty acid uptake in myocardium. In addition, total energy expenditure was increased in CAMCAKT mice, accompanied by reduced adiposity and reduced development of fatty liver. CONCLUSION: CAMDAKT mice modeled the effects of impaired mitochondrial signaling which occurs in the diabetic myocardium. Disruption of this pathway is a key step in the development of cardiomyopat

ObjectiveTo explore the population health impact of treating all US adults eligible for the Reduction of Cardiovascular Events with Icosapent Ethyl-Intervention Trial (REDUCE-IT) with icosapent ethyl (IPE), we estimated (1) the number of ASCVD events and healthcare costs that could be prevented; and (2) medication costs.MethodsWe derived REDUCE-IT eligible cohorts in (1) the National Health and Nutrition Examination Surveys (NHANES) 2009-2014 and (2) the Optum Research Database (ORD). Population sizes were obtained from NHANES and observed first event rates (composite of cardiovascular death, nonfatal myocardial infarction, nonfatal stroke, unstable angina requiring hospitalization, or coronary revascularization) were estimated from the ORD. Hazard ratios from REDUCE-IT USA estimated events prevented with IPE therapy. The National Inpatient Sample estimated event costs (facility and professional) and daily IPE treatment cost was approximated at $4.59.ResultsWe estimate 3.6 million US adults to be REDUCE-IT eligible, and the observed five-year first event rate without IPE of 19.0% (95% confidence interval [CI] 16.6%-19.5%) could be lowered to 13.1% (95% CI 12.8%-13.5%) with five years of IPE treatment, preventing 212,000 (uncertainty range 163,000-262,000) events. We projected the annual IPE treatment cost for all eligible persons to be $6.0 billion (95% CI $4.7-$7.5 billion), but saving $1.8 billion annually due to first events prevented (net annual cost $4.3 billion). The total five-year event rate (first and recurrent) could be reduced from 42.5% (95% CI 39.6%-45.4%) to 28.9% (95% CI 26.9-30.9%) with five years of IPE therapy, preventing 490,000 (uncertainty range 370,000-609,000) events (net annual cost $2.6 billion).ConclusionsTreating all REDUCE-IT eligible US adults has substantial medication costs but could prevent a substantial number of ASCVD events and associated direct costs. Indirect cost savings by preventing events could outweigh much of the incurred d

Although cardiovascular disease risk factors relate to COVID-19, the association of estimated atherosclerotic cardiovascular disease (ASCVD) risk with severe COVID-19 is not established. We examined the relation of the pooled-cohort ASCVD risk score to severe COVID-19 among 28,646 subjects from the National COVID Cohort Collaborative database who had positive SARS-CoV-2 test results from April 1, 2020 to April 1, 2021. In addition, 10-year ASCVD risk scores were calculated, and subjects were stratified into low-risk (<5%), borderline-risk (5% to <7.5%), intermediate-risk (7.5% to <20%), and high-risk (>=20%) groups. Severe COVID-19 outcomes (including death, remdesivir treatment, COVID-19 pneumonia, acute respiratory distress syndrome, and mechanical ventilation) occurring during follow-up were examined individually and as a composite in relation to ASCVD risk group across race and gender. Multiple logistic regression, adjusted for age, gender, and race, examined the relation of ASCVD risk group to the odds of severe COVID-19 outcomes. Our subjects had a mean age of 59.4 years; 14% were black and 57% were female. ASCVD risk group was directly related to severe COVID-19 prevalence. The adjusted odds ratio of the severe composite COVID-19 outcome by risk group (vs the low-risk group) was 1.8 (95% confidence interval 1.5 to 2.2) for the borderline-risk, 2.7 (2.3 to 3.2) for the intermediate-risk, and 4.6 (3.7 to 5.6) for the high-risk group. Black men and black women in the high-risk group showed higher severe COVID-19 prevalence compared with nonblack men and nonblack women. Prevalence of severe COVID-19 outcomes was similar in intermediate-risk black men and high-risk nonblack men (approximately 12%). In conclusion, although further research is needed, the 10-year ASCVD risk score in adults ages 40 to 79 years may be used to identify those who are at highest risk for COVID-19 complications and for whom more intensive treatment may be warranted.

IntroductionHypertriglyceridemia is associated with increased atherosclerotic cardiovascular disease (ASCVD) event risk, which persists even in statin-treated patients. The objective of this analysis was to estimate the prevalence of triglyceride (TG) levels ≥ 150 mg/dl in statin-treated adults with diabetes or ASCVD in the United States.MethodsLaboratory data, medical history, and prescription data from 40,617 subjects who participated in the US National Health and Nutrition Examination Survey (NHANES) spanning 8 years (four 2-year surveys; 2007-2014) were analyzed. Patients included were ≥ 20 years old and had morning fasting (at least 8.5 h) TG values available. The proportion and weighted number of individuals in the US population with TG ≥ 150 mg/dl was calculated according to statin use, as well as in key subgroups of statin-treated patients including those with low-density lipoprotein cholesterol (LDL-C) levels < 100 mg/dl, type 2 diabetes, ASCVD, and those with type 2 diabetes and ASCVD.ResultsA total of 9593 subjects, projected to represent 219.9 million Americans, met the study entry criteria and were included in the analysis. Of these, 2523 had TG levels ≥ 150 mg/dl, translating to a prevalence of 25.9% and representing 56.9 million Americans. Among statin-treated adults, the proportion with TG levels ≥ 150 mg/dl was 31.6% (12.3 million) and ranged from 27.6 to 39.5% for those who also had LDL-C levels < 100 mg/dl and type 2 diabetes or ASCVD.ConclusionsOver 12 million Americans are treated with a statin and have TG levels ≥ 150 mg/dl. Interventions such as icosapent ethyl that have been shown to reduce ASCVD event risk in this elevated TG population with type 2 diabetes or established ASCVD can provide substantial clinical benefit for these patients.

IntroductionHypertriglyceridemia is associated with increased atherosclerotic cardiovascular disease (ASCVD) event risk, which persists even in statin-treated patients. The objective of this analysis was to estimate the prevalence of triglyceride (TG) levels ≥ 150 mg/dl in statin-treated adults with diabetes or ASCVD in the United States.MethodsLaboratory data, medical history, and prescription data from 40,617 subjects who participated in the US National Health and Nutrition Examination Survey (NHANES) spanning 8 years (four 2-year surveys; 2007-2014) were analyzed. Patients included were ≥ 20 years old and had morning fasting (at least 8.5 h) TG values available. The proportion and weighted number of individuals in the US population with TG ≥ 150 mg/dl was calculated according to statin use, as well as in key subgroups of statin-treated patients including those with low-density lipoprotein cholesterol (LDL-C) levels < 100 mg/dl, type 2 diabetes, ASCVD, and those with type 2 diabetes and ASCVD.ResultsA total of 9593 subjects, projected to represent 219.9 million Americans, met the study entry criteria and were included in the analysis. Of these, 2523 had TG levels ≥ 150 mg/dl, translating to a prevalence of 25.9% and representing 56.9 million Americans. Among statin-treated adults, the proportion with TG levels ≥ 150 mg/dl was 31.6% (12.3 million) and ranged from 27.6 to 39.5% for those who also had LDL-C levels < 100 mg/dl and type 2 diabetes or ASCVD.ConclusionsOver 12 million Americans are treated with a statin and have TG levels ≥ 150 mg/dl. Interventions such as icosapent ethyl that have been shown to reduce ASCVD event risk in this elevated TG population with type 2 diabetes or established ASCVD can provide substantial clinical benefit for these patients.

AimTo investigate multiple risk factor target attainment in adults with diabetes mellitus (DM) for atherosclerotic cardiovascular disease (ASCVD) prevention and the predictors of such attainment in a contemporary DM registry.MethodsIn the US Diabetes Collaborative Registry we identified patients who were at target for glycated haemoglobin (HbA1c; < 53 mmol/mol (7%) or < 64 mmol/mol (8%) if with ASCVD), LDL cholesterol (< 2.6 mmol/L (100 mg/dL) or < 1.8 mmol/L (70 mg/dL) 1.8 if with ASCVD) and blood pressure (BP; <140/90 mmHg and < 130/80 mmHg as an alternate), and who had non-smoking status, by sex, race and history of ASCVD. Multiple logistic regression was used to examine predictors of target attainment.ResultsIn 74 393 patients with DM who had available data (mean age 69.0 years, 41.0% women), overall target attainment for HbA1c, BP, LDL cholesterol and non-smoking status was 73.6%, 69.0% (40.3% for BP <130/80 mmHg), 48.6% and 85.2%, respectively. Only 21.6% (13.0% with BP <130/80 mmHg) were at target for all four measures, and the proportions were higher in men (23.6%) versus women (18.6%) and in white people (22.5%) versus African-American people (14.7%) and people of other races (20.8%; P < 0.01). A total of 62.4% were on a moderate-/high-intensity statin. Age (≥65 years: odds ratio [OR] 1.9, 95% confidence interval [CI] 1.7-2.0; and 55-64 years: OR 1.3, 95% CI 1.2-1.4 vs. <55 years), male sex (OR 1.3, 95% CI 1.3-1.4), white race (OR 1.4, 95% CI 1.3-1.5), middle or high income (ORs 1.1, 95% CI 1.1-1.2 or 1.4, 95% CI 1.4-1.5, respectively) were associated, and depression (OR 0.9, 95% CI 0.8-1.0) was inversely associated with meeting all four targets (all P = 0.01 to P < 0.001).ConclusionsIn our US registry of patients with DM, only one in five patients were achieving comprehensive risk factor control. Multifactorial interventions will be necessary to optimize ASCVD risk factor control.

AimTo investigate multiple risk factor target attainment in adults with diabetes mellitus (DM) for atherosclerotic cardiovascular disease (ASCVD) prevention and the predictors of such attainment in a contemporary DM registry.MethodsIn the US Diabetes Collaborative Registry we identified patients who were at target for glycated haemoglobin (HbA1c; < 53 mmol/mol (7%) or < 64 mmol/mol (8%) if with ASCVD), LDL cholesterol (< 2.6 mmol/L (100 mg/dL) or < 1.8 mmol/L (70 mg/dL) 1.8 if with ASCVD) and blood pressure (BP; <140/90 mmHg and < 130/80 mmHg as an alternate), and who had non-smoking status, by sex, race and history of ASCVD. Multiple logistic regression was used to examine predictors of target attainment.ResultsIn 74 393 patients with DM who had available data (mean age 69.0 years, 41.0% women), overall target attainment for HbA1c, BP, LDL cholesterol and non-smoking status was 73.6%, 69.0% (40.3% for BP <130/80 mmHg), 48.6% and 85.2%, respectively. Only 21.6% (13.0% with BP <130/80 mmHg) were at target for all four measures, and the proportions were higher in men (23.6%) versus women (18.6%) and in white people (22.5%) versus African-American people (14.7%) and people of other races (20.8%; P < 0.01). A total of 62.4% were on a moderate-/high-intensity statin. Age (≥65 years: odds ratio [OR] 1.9, 95% confidence interval [CI] 1.7-2.0; and 55-64 years: OR 1.3, 95% CI 1.2-1.4 vs. <55 years), male sex (OR 1.3, 95% CI 1.3-1.4), white race (OR 1.4, 95% CI 1.3-1.5), middle or high income (ORs 1.1, 95% CI 1.1-1.2 or 1.4, 95% CI 1.4-1.5, respectively) were associated, and depression (OR 0.9, 95% CI 0.8-1.0) was inversely associated with meeting all four targets (all P = 0.01 to P < 0.001).ConclusionsIn our US registry of patients with DM, only one in five patients were achieving comprehensive risk factor control. Multifactorial interventions will be necessary to optimize ASCVD risk factor control.

n/a - brief communication.

ObjectiveThe prevalence of diabetes mellitus (DM) is increasing rapidly, particularly in Asia. Asian immigrants in Western countries are a fast-growing population who carry both intrinsic risks due to their genetic background and extrinsic risks associated with Western lifestyles. However, recent trends in diabetes prevalence and associated risk factors among Asian immigrants in the USA are not well understood.Research design and methodsWe examined adults aged 18 and older from the recent California Health Interview Survey data sets from 2003 to 2013 to determine prevalence of known DM among first-generation Asian immigrants and whites. The impact of various DM risk factors in Asian immigrants relative to whites was analyzed and multivariable regression models were constructed to obtain adjusted DM risk in Asian immigrants versus in whites.ResultsAcross the study span, we identified 2007 first-generation Asian immigrants and 14 668 whites as having known DM or prediabetes mellitus (pre-DM). From 2003 to 2013, the prevalence of DM and pre-DM combined rose from 6.8% to 12.4% in Asian immigrants and 5.5% to 6.9% in whites. Much of the increase could be attributed to pre-DM, which rose from 0.7% to 3.2% in Asian immigrants during the study period. The impacts of age and body mass index on DM risk were consistently greater in Asian immigrants than in whites. Non-DM Asian immigrants were found less likely to engage in physical activity than were non-DM whites. After adjustment of various associated factors, Asian immigrants were more likely than whites to have DM and this relative risk for DM gradually increased across the study period.ConclusionsA rising prevalence of known DM and particularly pre-DM among Asian immigrants in California was observed during the previous decade. To reduce the burden of diabetes and its complications, future strategies should consider specific risk factors for this ethnic group, including encouraging physical activity.

ObjectiveThe prevalence of diabetes mellitus (DM) is increasing rapidly, particularly in Asia. Asian immigrants in Western countries are a fast-growing population who carry both intrinsic risks due to their genetic background and extrinsic risks associated with Western lifestyles. However, recent trends in diabetes prevalence and associated risk factors among Asian immigrants in the USA are not well understood.Research design and methodsWe examined adults aged 18 and older from the recent California Health Interview Survey data sets from 2003 to 2013 to determine prevalence of known DM among first-generation Asian immigrants and whites. The impact of various DM risk factors in Asian immigrants relative to whites was analyzed and multivariable regression models were constructed to obtain adjusted DM risk in Asian immigrants versus in whites.ResultsAcross the study span, we identified 2007 first-generation Asian immigrants and 14 668 whites as having known DM or prediabetes mellitus (pre-DM). From 2003 to 2013, the prevalence of DM and pre-DM combined rose from 6.8% to 12.4% in Asian immigrants and 5.5% to 6.9% in whites. Much of the increase could be attributed to pre-DM, which rose from 0.7% to 3.2% in Asian immigrants during the study period. The impacts of age and body mass index on DM risk were consistently greater in Asian immigrants than in whites. Non-DM Asian immigrants were found less likely to engage in physical activity than were non-DM whites. After adjustment of various associated factors, Asian immigrants were more likely than whites to have DM and this relative risk for DM gradually increased across the study period.ConclusionsA rising prevalence of known DM and particularly pre-DM among Asian immigrants in California was observed during the previous decade. To reduce the burden of diabetes and its complications, future strategies should consider specific risk factors for this ethnic group, including encouraging physical activity.

BackgroundThough it is a widely held belief that caffeinated beverages predispose individuals to arrhythmias, it is not clear whether regular coffee consumption is associated with development of atrial fibrillation (AF).ObjectiveWe examined the association between long-term coffee consumption and development of AF in both habitual (≥0.5 cups of daily coffee) and nonhabitual (<0.5 cups/day) drinkers.MethodsA total of 5,972 men and women, aged 45-84 years and without a history of cardiovascular disease at baseline in the Multi-Ethnic Study of Atherosclerosis (MESA) were followed from 2000 to 2014 for incident AF with baseline coffee consumption assessed in 2000-2002 via a Food Frequency Questionnaire and divided into quartiles of 0 cups/day, >0 to <0.5 cups/day, ≥0.5 to 1.5 cups/day, and ≥1.5 cups/day.ResultsOut of the 828 incident cases of AF, intermittent coffee consumption (>0 to 0.5 cups of daily coffee) was associated with a greater risk of incident AF (HR 1.22, 95% CI 1.01-1.48) relative to 0 cups/day in multivariable Cox proportional hazards models after adjustment for numerous AF risk factors. This relation was particularly pronounced in men (adjusted HR=1.36, 95% CI 1.04-1.77). Higher coffee consumption was not associated with AF risk (HR 1.03, 95%CI 0.93-1.14 for ≥0.5 to 1.5 cups/day and 1.05, 95%CI 0.97-1.13 for ≥1.5 cups/day).ConclusionsWhile there appears to be no dose-response association between habitual coffee intake and AF risk, we found evidence that intermittent, but not habitual, coffee consumption is associated with a modestly increased risk of incident AF that deserves further study.

BackgroundHigh-sensitivity cardiac troponin T (hs-cTnT) is individually associated with incident hypertension (HTN) and cardiovascular disease (CVD) events. We hypothesize that the increases in hs-cTnT with increases in blood pressure will be related to higher incidence of CVD.MethodsThe Cardiovascular Health Study is a longitudinal cohort of older adults. Those with hs-cTnT data and CVD risk factors at baseline and follow-up (2-3 years later) were stratified based on systolic blood pressure (SBP; optimal: <120 mm Hg, intermediate: 120-139 mm Hg, elevated: ≥140 mm Hg) and hs-cTnT (undetectable: <5 ng/l, detectable: 5-13 ng/l, elevated: ≥14 ng/l) categories. SBP and hs-cTnT were classified as increased or decreased if they changed categories between exams, and stable if they did not. Cox regression evaluated incident CVD events over an average 9-year follow-up.ResultsAmong 2,219 adults, 510 (23.0 %) had decreased hs-cTnT, 1,279 (57.6 %) had stable hs-cTnT, and 430 (19.4 %) had increased hs-cTnT. Those with increased hs-cTnT had a higher CVD risk with stable SBP (hazard ratio [HR]: 1.28 [1.04-1.57], P = 0.02) or decreased SBP (HR: 1.57 [1.08-2.28], P = 0.02) compared to those within the same SBP group but a stable hs-cTnT. In those with lower SBP at follow-up, there was an inverse relation between diastolic blood pressure (DBP) and risk of CVD events in those with increased hs-cTnT (HR: 0.44 per 10 mm Hg increase, P < 0.01).ConclusionAn increase in hs-cTnT over time is associated with a higher risk of CVD even when the blood pressure is stable or decreases over time.

Honeypots are designed to trap the attacker with the purpose of investigating its malicious behavior. Owing to the increasing variety and sophistication of cyber attacks, how to capture high-quality attack data has become a challenge in the context of honeypot area. All-round honeypots, which mean a significant improvement in sensibility, countermeasure, and stealth, are necessary to tackle the problem. In this paper, we propose a novel honeypot architecture termed HoneyDOC to support all-round honeypot design and implementation. Our HoneyDOC architecture clearly identifies three essential independent and collaborative modules, Decoy, Captor, and Orchestrator. Based on the efficient architecture, a software-defined networking-enabled honeypot system is designed, which supplies a high programmability for technically sustaining the features for capturing high-quality data. A proof-of-concept system is implemented to validate its feasibility and effectiveness. The experimental results show the benefits by using the proposed architecture compared with the previous honeypot solutions.

Cyber-attacks affect every aspect of our lives. These attacks have serious consequences, not only for cyber-security, but also for safety, as the cyber and physical worlds are increasingly linked. Providing effective cyber-security requires cooperation and collaboration among all the entities involved. Increasing the amount of cyber threat information (CTI) available for analysis allows better prediction, prevention and mitigation of cyber-attacks. However, organizations are deterred from sharing their CTI over concerns that sensitive and confidential information may be revealed to others. We address this concern by providing a flexible framework that allows the confidential sharing of CTI for analysis between collaborators. We propose a five-level trust model for a cloud-edge based data sharing infrastructure. The data owner can choose an appropriate trust level and CTI data sanitization approach, ranging from plain text, through anonymization/pseudonymization to homomorphic encryption, in order to manipulate the CTI data prior to sharing it for analysis. Furthermore, this sanitization can be performed by either an edge device or by the cloud service provider, depending upon the level of trust the organization has in the latter. We describe our trust model, our cloud-edge infrastructure, and its deployment model, which are designed to satisfy the broadest range of requirements for confidential CTI data sharing. Finally we briefly describe our implementation and the testing that has been carried out so far by four pilot projects that are validating our infrastructure.

Honeypots are designed to trap the attacker with the purpose of investigating its malicious behaviour. Owing to the increasing variety and sophistication of cyber attacks, how to capture high-quality attack data has become a challenge in the context of honeypot area. All-round honeypots, which mean a significant improvement in sensibility, countermeasure, and stealth, are necessary to tackle the problem. In this paper, we propose a novel honeypot architecture termed HoneyDOC to support all-round honeypot design and implementation. Our HoneyDOC architecture clearly identifies three essential independent and collaborative modules, Decoy, Captor, and Orchestrator. Based on the efficient architecture, a software-defined networking-enabled honeypot system is designed, which supplies a high programmability for technically sustaining the features for capturing high-quality data. A proof-of-concept system is implemented to validate its feasibility and effectiveness. The experimental results show the benefits by using the proposed architecture compared with the previous honeypot solutions.

Honeypots are designed to trap the attacker with the purpose of investigating its malicious behaviour. Owing to the increasing variety and sophistication of cyber attacks, how to capture high-quality attack data has become a challenge in the context of honeypot area. All-round honeypots, which mean a significant improvement in sensibility, countermeasure, and stealth, are necessary to tackle the problem. In this paper, we propose a novel honeypot architecture termed HoneyDOC to support all-round honeypot design and implementation. Our HoneyDOC architecture clearly identifies three essential independent and collaborative modules, Decoy, Captor, and Orchestrator. Based on the efficient architecture, a software-defined networking-enabled honeypot system is designed, which supplies a high programmability for technically sustaining the features for capturing high-quality data. A proof-of-concept system is implemented to validate its feasibility and effectiveness. The experimental results show the benefits by using the proposed architecture compared with the previous honeypot solutions.

Objectives: Asians are at high risk for diabetes mellitus (DM), yet there is little population-based information about DM prevalence among subgroups of immigrants and US-born Asians in the US over time. The purpose of this study is to compare the prevalence and odds ratios (ORs) of DM between Asian and Asian subgroup immigrants and White in California using data from 2003, 2005, 2007, 2009, 2011, 2013 California Health Interview Survey (CHIS).Methods: Six CHIS datasets were analyzed and compared to determine the prevalence of DM among Asian Immigrants and White from 2003 to 2013. Subjects who reported a physician-diagnosis of diabetes (type 1 and type 2) were considered to have DM (borderline/prediabetes and gestational diabetes were not included). Multivariable logistic regression models were built to calculate adjusted ORs of DM and to find the potential explanations for the differences in the prevalence results.Results: There was a trend towards higher rate of diabetes for both Asian Immigrants and White in California over the last decade. Asian Immigrants had a continuously higher prevalence of DM compared to White, and the difference was greater especially in 2013. Prevalence of DM varied greatly among Asian subgroups with Filipinos and Koreans being two highest groups. After adjusting for confounding variables (age, sex, obesity, education attainment, insurance status, English proficiency, smoking status, taking medication to control high blood pressure), the ORs of DM between Asian Immigrants and White were 1.71(0.96-3.05), 1.31(0.81-2.13), 1.22(0.79-1.89), 2.19*(1.13-4.22), 1.81*(1.15-2.87), 2.09*(1.05-4.18) for 2003, 2005, 2007, 2009, 2011 and 2013 CHIS dataset respectively. Conclusions: Overall, Asian Immigrants were more likely to develop DM compared to White in California during the last decade, even after controlling for confounding variables. Great variability exists in DM prevalence among Asian subgroups and this should be considered in future studies

Objectives: Asians are at high risk for diabetes mellitus (DM), yet there is little population-based information about DM prevalence among subgroups of immigrants and US-born Asians in the US over time. The purpose of this study is to compare the prevalence and odds ratios (ORs) of DM between Asian and Asian subgroup immigrants and White in California using data from 2003, 2005, 2007, 2009, 2011, 2013 California Health Interview Survey (CHIS).Methods: Six CHIS datasets were analyzed and compared to determine the prevalence of DM among Asian Immigrants and White from 2003 to 2013. Subjects who reported a physician-diagnosis of diabetes (type 1 and type 2) were considered to have DM (borderline/prediabetes and gestational diabetes were not included). Multivariable logistic regression models were built to calculate adjusted ORs of DM and to find the potential explanations for the differences in the prevalence results.Results: There was a trend towards higher rate of diabetes for both Asian Immigrants and White in California over the last decade. Asian Immigrants had a continuously higher prevalence of DM compared to White, and the difference was greater especially in 2013. Prevalence of DM varied greatly among Asian subgroups with Filipinos and Koreans being two highest groups. After adjusting for confounding variables (age, sex, obesity, education attainment, insurance status, English proficiency, smoking status, taking medication to control high blood pressure), the ORs of DM between Asian Immigrants and White were 1.71(0.96-3.05), 1.31(0.81-2.13), 1.22(0.79-1.89), 2.19*(1.13-4.22), 1.81*(1.15-2.87), 2.09*(1.05-4.18) for 2003, 2005, 2007, 2009, 2011 and 2013 CHIS dataset respectively. Conclusions: Overall, Asian Immigrants were more likely to develop DM compared to White in California during the last decade, even after controlling for confounding variables. Great variability exists in DM prevalence among Asian subgroups and this should be considered in future studies

A honeypot is a type of security facility deliberately created to be probed, attacked and compromised. It is often used for protecting production systems by detecting and deflecting unauthorized accesses. It is also useful for investigating the behaviour of attackers, and in particular, unknown attacks. For the past 17 years much effort has been invested in the research and development of honeypot based techniques and tools and they have evolved to become an increasingly powerful means of defending against the creations of the blackhat community. In this paper, by studying multiple honeypot systems, the two essential elements of honeypots - the decoy and the security program - are captured and presented, together with two abstract organizational forms - independent and cooperative - in which these two elements can be integrated. A novel decoy and security program (D-P) based taxonomy is proposed, for the purpose of investigating and classifying the various techniques involved in honeypot systems. An extensive set of honeypot projects and research, which cover the techniques applied in both independent and cooperative honeypots, is surveyed under the taxonomy framework. Finally, the taxonomy is applied to a wide set of tools and systems in order to demonstrate its validity and predict the tendency of honeypot development.

A honeypot is a type of security facility deliberately created to be probed, attacked and compromised. It is often used for protecting production systems by detecting and deflecting unauthorized accesses. It is also useful for investigating the behaviour of attackers, and in particular, unknown attacks. For the past 17 years much effort has been invested in the research and development of honeypot based techniques and tools and they have evolved to become an increasingly powerful means of defending against the creations of the blackhat community. In this paper, by studying multiple honeypot systems, the two essential elements of honeypots - the decoy and the security program - are captured and presented, together with two abstract organizational forms - independent and cooperative - in which these two elements can be integrated. A novel decoy and security program (D-P) based taxonomy is proposed, for the purpose of investigating and classifying the various techniques involved in honeypot systems. An extensive set of honeypot projects and research, which cover the techniques applied in both independent and cooperative honeypots, is surveyed under the taxonomy framework. Finally, the taxonomy is applied to a wide set of tools and systems in order to demonstrate its validity and predict the tendency of honeypot development.

Increasing numbers of Small and Medium Enterprises (SME) are outsourcing or hosting their services on different Cloud Service Providers (CSP). They are also using different security services from these CSPs such as firewalls, intrusion detection/prevention systems and anti-malware. Although for the SMEs the main purpose of using these security services is to protect their cyber assets, either physical or virtual, from security threats and compromises, a very useful and valuable by-product of these security services is the wealth of Cyber Threat Information (CTI) that is collected over time. However, a common problem faced by SMEs is that they lack the resources and expertise for monitoring, analysing and reacting to any security notifications, alerts or events generated by the security services they have subscribed to. An obvious solution to this problem is that the SMEs outsource this problem to a cloud based service as well, by sharing their CTI with this service and allowing it to analyse the information and generate actionable reports or patches. The more CTI obtained from different SMEs, the better the analysis result. In this paper, we try to address some of the privacy and confidentiality issues that arise as a result of different SMEs sharing their CTI with such a third party analysis service for the aggregate analysis scenario we just described. We present the design and architecture of our solution that aims to allow SMEs to perform policy-based sharing of CTI, while also offering them flexible privacy and confidentiality controls.

Honeypots have been largely used to capture and investigate malicious behavior through deliberately sacrificing their own resources in order to be attacked. Hybrid honeypot architectures consisting of frontends and backends are widely used in the research area, specially due to the benefits of their high scalability and fidelity for detailed attacking data collection. A hybrid honeypot system often needs a facility aimed to tightly control the network traffic, for purposes such as redirecting the traffic from the frontends to the backends for in-depth attack analysis. However, the current traffic redirection approaches, particularly the TCP connection handover mechanisms, are not stealthy and they can be easily detected by attackers.This paper proposes an SDN based network data controller for hybrid honeypot systems that uses a transparent TCP connection handover mechanism and provides a traffic filtering approach based on the Snort alert functionality. The controller is implemented as an application based on the open-source Ryu SDN framework. It allows the users to configure their own network data control rules, which based on the Snort alert messages will forward or redirect the traffic to the corresponding honeypots. The experiments validate the proposed mechanism and the testing results show that the controller can efficiently perform the stealthy TCP connection handover as well.

A honeypot is a type of security facility deliberately created to be probed, attacked and compromised. It is often used for protecting production systems by detecting and deflecting unauthorized accesses. It is also useful for investigating the behaviour of attackers, and in particular, unknown attacks. For the past 17 years much effort has been invested in the research and development of honeypot based techniques and tools and they have evolved to become an increasingly powerful means of defending against the creations of the blackhat community. In this paper, by studying multiple honeypot systems, the two essential elements of honeypots - the decoy and the security program - are captured and presented, together with two abstract organizational forms - independent and cooperative - in which these two elements can be integrated. A novel decoy and security program (D-P) based taxonomy is proposed, for the purpose of investigating and classifying the various techniques involved in honeypot systems. An extensive set of honeypot projects and research, which cover the techniques applied in both independent and cooperative honeypots, is surveyed under the taxonomy framework. Finally, the taxonomy is applied to a wide set of tools and systems in order to demonstrate its validity and predict the tendency of honeypot development.

A honeypot is a type of security facility deliberately created to be probed, attacked and compromised. It is often used for protecting production systems by detecting and deflecting unauthorized accesses. It is also useful for investigating the behaviour of attackers, and in particular, unknown attacks. For the past 17 years much effort has been invested in the research and development of honeypot based techniques and tools and they have evolved to become an increasingly powerful means of defending against the creations of the blackhat community. In this paper, by studying multiple honeypot systems, the two essential elements of honeypots - the decoy and the security program - are captured and presented, together with two abstract organizational forms - independent and cooperative - in which these two elements can be integrated. A novel decoy and security program (D-P) based taxonomy is proposed, for the purpose of investigating and classifying the various techniques involved in honeypot systems. An extensive set of honeypot projects and research, which cover the techniques applied in both independent and cooperative honeypots, is surveyed under the taxonomy framework. Finally, the taxonomy is applied to a wide set of tools and systems in order to demonstrate its validity and predict the tendency of honeypot development.

Social engineering is the attack aimed to manipulate dupe to divulge sensitive information or take actions to help the adversary bypass the secure perimeter in front of the information-related resources so that the attacking goals can be completed. Though there are a number of security tools, such as firewalls and intrusion detection systems which are used to protect machines from being attacked, widely accepted mechanism to prevent dupe from fraud is lacking. However, the human element is often the weakest link of an information security chain, especially, in a human-centered environment. In this paper, we reveal that the human psychological weaknesses result in the main vulnerabilities that can be exploited by social engineering attacks. Also, we capture two essential levels, internal characteristics of human nature and external circumstance influences, to explore the root cause of the human weaknesses. We unveil that the internal characteristics of human nature can be converted into weaknesses by external circumstance influences. So, we propose the I-E based model of human weakness for social engineering investigation. Based on this model, we analyzed the vulnerabilities exploited by different techniques of social engineering, and also, we conclude several defense approaches to fix the human weaknesses. This work can help the security researchers to gain insights into social engineering from a different perspective, and in particular, enhance the current and future research on social engineering defense mechanisms.

Social engineering is the attack aimed to manipulate dupe to divulge sensitive information or take actions to help the adversary bypass the secure perimeter in front of the information-related resources so that the attacking goals can be completed. Though there are a number of security tools, such as firewalls and intrusion detection systems which are used to protect machines from being attacked, widely accepted mechanism to prevent dupe from fraud is lacking. However, the human element is often the weakest link of an information security chain, especially, in a human-centered environment. In this paper, we reveal that the human psychological weaknesses result in the main vulnerabilities that can be exploited by social engineering attacks. Also, we capture two essential levels, internal characteristics of human nature and external circumstance influences, to explore the root cause of the human weaknesses. We unveil that the internal characteristics of human nature can be converted into weaknesses by external circumstance influences. So, we propose the I-E based model of human weakness for social engineering investigation. Based on this model, we analyzed the vulnerabilities exploited by different techniques of social engineering, and also, we conclude several defense approaches to fix the human weaknesses. This work can help the security researchers to gain insights into social engineering from a different perspective, and in particular, enhance the current and future research on social engineering defense mechanisms.

Honeypots have been largely used to capture and investigate malicious behavior through deliberately sacrificing their own resources in order to be attacked. Hybrid honeypot architectures consisting of frontends and backends are widely used in the research area, specially due to the benefits of their high scalability and fidelity for detailed attacking data collection. A hybrid honeypot system often needs a facility aimed to tightly control the network traffic, for purposes such as redirecting the traffic from the frontends to the backends for in-depth attack analysis. However, the current traffic redirection approaches, particularly the TCP connection handover mechanisms, are not stealthy and they can be easily detected by attackers.This paper proposes an SDN based network data controller for hybrid honeypot systems that uses a transparent TCP connection handover mechanism and provides a traffic filtering approach based on the Snort alert functionality. The controller is implemented as an application based on the open-source Ryu SDN framework. It allows the users to configure their own network data control rules, which based on the Snort alert messages will forward or redirect the traffic to the corresponding honeypots. The experiments validate the proposed mechanism and the testing results show that the controller can efficiently perform the stealthy TCP connection handover as well.

A honeypot is a type of security facility deliberately created to be probed, attacked and compromised. It is often used for protecting production systems by detecting and deflecting unauthorized accesses. It is also useful for investigating the behaviour of attackers, and in particular, unknown attacks. For the past 17 years much effort has been invested in the research and development of honeypot based techniques and tools and they have evolved to become an increasingly powerful means of defending against the creations of the blackhat community. In this paper, by studying multiple honeypot systems, the two essential elements of honeypots - the decoy and the security program - are captured and presented, together with two abstract organizational forms - independent and cooperative - in which these two elements can be integrated. A novel decoy and security program (D-P) based taxonomy is proposed, for the purpose of investigating and classifying the various techniques involved in honeypot systems. An extensive set of honeypot projects and research, which cover the techniques applied in both independent and cooperative honeypots, is surveyed under the taxonomy framework. Finally, the taxonomy is applied to a wide set of tools and systems in order to demonstrate its validity and predict the tendency of honeypot development.

Honeypots are designed to investigate malicious behavior. Each type of homogeneous honeypot system has its own characteristics in respect of specific security functionality, and also suffers functional drawbacks that restrict its application scenario. In practical scenarios, therefore, security researchers always need to apply heterogeneous honeypots to cope with different attacks. However, there is a lack of general tools or platforms that can support versatile honeynet deployment in order to investigate the malicious behavior. In this study, the authors propose a versatile virtual honeynet management tool to address this problem. It is a flexible tool that offers security researchers the versatility to deploy various types of honeypots. It can also generate and manage the virtual honeynet through a dynamic configuration approach adapting to the mutable network environment. The experimental results demonstrate that this tool is effective to perform automated honeynet deployment toward a variety of heterogeneous honeypots.

Honeypots are designed to investigate malicious behavior. Each type of homogeneous honeypot system has its own characteristics in respect of specific security functionality, and also suffers functional drawbacks that restrict its application scenario. In practical scenarios, therefore, security researchers always need to apply heterogeneous honeypots to cope with different attacks. However, there is a lack of general tools or platforms that can support versatile honeynet deployment in order to investigate the malicious behavior. In this study, the authors propose a versatile virtual honeynet management tool to address this problem. It is a flexible tool that offers security researchers the versatility to deploy various types of honeypots. It can also generate and manage the virtual honeynet through a dynamic configuration approach adapting to the mutable network environment. The experimental results demonstrate that this tool is effective to perform automated honeynet deployment toward a variety of heterogeneous honeypots.

One of the major challenges in cloud computing and data centers is the energy conservation and emission reduction. Accurate prediction algorithms are essential for building energy efficient storage systems in cloud computing. In this paper, we first propose a Three-State Disk Model (3SDM), which can describe the service quality and energy consumption states of a storage system accurately. Based on this model, we develop a method for achieving energy conservation without losing quality by skewing the workload among the disks to transmit the disk states of a storage system. The efficiency of this method is highly dependent on the accuracy of the information predicting the blocks to be accessed and the blocks not be accessed in the near future. We develop a priori information and sliding window based prediction (PISWP) algorithm by taking advantage of the priori information about human behavior and selecting suitable size of sliding window. The PISWP method targets at streaming media applications, but we also check its efficiency on other two applications, news in webpage and new tool released. Disksim, an established storage system simulator, is applied in our experiments to verify the effect of our method for various users’ traces. The results show that this prediction method can bring a high degree energy saving for storage systems in cloud computing environment.

One of the major challenges in cloud computing and data centers is the energy conservation and emission reduction. Accurate prediction algorithms are essential for building energy efficient storage systems in cloud computing. In this paper, we first propose a Three-State Disk Model (3SDM), which can describe the service quality and energy consumption states of a storage system accurately. Based on this model, we develop a method for achieving energy conservation without losing quality by skewing the workload among the disks to transmit the disk states of a storage system. The efficiency of this method is highly dependent on the accuracy of the information predicting the blocks to be accessed and the blocks not be accessed in the near future. We develop a priori information and sliding window based prediction (PISWP) algorithm by taking advantage of the priori information about human behavior and selecting suitable size of sliding window. The PISWP method targets at streaming media applications, but we also check its efficiency on other two applications, news in webpage and new tool released. Disksim, an established storage system simulator, is applied in our experiments to verify the effect of our method for various users’ traces. The results show that this prediction method can bring a high degree energy saving for storage systems in cloud computing environment.