Modeling multivariate cyber risks: deep learning dating extreme value theory.

Modeling cyber risks has been an important but challenging task in the domain of cyber security, which is mainly caused by the high dimensionality and heavy tails of risk patterns. Those obstacles have hindered the development of statistical modeling of the multivariate cyber risks. In this work, we propose a novel approach for modeling the multivariate cyber risks which relies on the deep learning and extreme value theory. The proposed model not only enjoys the high accurate point predictions via deep learning but also can provide the satisfactory high quantile predictions via extreme value theory. Both the simulation and empirical studies show that the proposed approach can model the multivariate cyber risks very well and provide satisfactory prediction performances.

Statistical modeling of computer malware propagation dynamics in cyberspace.

Modeling cyber threats, such as the computer malicious software (malware) propagation dynamics in cyberspace, is an important research problem because models can deepen our understanding of dynamical cyber threats. In this paper, we study the statistical modeling of the macro-level evolution of dynamical cyber attacks. Specifically, we propose a Bayesian structural time series approach for modeling the computer malware propagation dynamics in cyberspace. Our model not only possesses the parsimony property (i.e. using few model parameters) but also can provide the predictive distribution of the dynamics by accommodating uncertainty. Our simulation study shows that the proposed model can fit and predict the computer malware propagation dynamics accurately, without requiring to know the information about the underlying attack-defense interaction mechanism and the underlying network topology. We use the model to study the propagation of two particular kinds of computer malware, namely the Conficker and Code Red worms, and show that our model has very satisfactory fitting and prediction accuracies.

Anti-diabetic activity of Vaccinium bracteatum Thunb. leaves' polysaccharide in STZ-induced diabetic mice.

Vaccinium bracteatum Thunb. (VBT) is a traditional Chinese herbal medicine. The anti-diabetic activity of VBT leaves' polysaccharide (VBTLP) is studied in this paper. The results indicated VBTLP had a dose-dependent decrease on the blood glucose (BG) level, and the time effect of VBTLP on BG level was also significant. The insulin level of high dose group (HDG) was significantly higher (p<0.05) than that of model control (MC) group. Compared to MC, HDG and lose dose group (LDG) had significantly lower (p<0.05) TC and LDL-C levels, however, TG and HDL-C levels are similar. Compared to non-diabetic control (NC), HDG and LDG had similar plasma lipid levels except for higher LDL-C level. Although body weights of LDG and HDG were significant lower (p<0.05) than that of NC from week 2 to week 6, they were similar to that of PC. The results indicate VBTLP possesses a potential hypoglycemic effect in streptozotocin-induced diabetic mice.

Comparison of pharmacy-based measures of medication adherence.

BACKGROUND: Pharmacy databases are commonly used to assess medication usage, and a number of measures have been developed to measure patients' adherence to medication. An extensive literature now supports these measures, although few studies have systematically compared the properties of different adherence measures. METHODS: As part of an 18-month randomized clinical trial to assess the impact of automated telephone reminders on adherence to inhaled corticosteroids (ICS) among 6903 adult members of a managed care organization, we computed eight pharmacy-based measures of ICS adherence using outpatient pharmacy dispensing records obtained from the health plan's electronic medical record. We used simple descriptive statistics to compare the relative performance characteristics of these measures. RESULTS: Comparative analysis found a relative upward bias in adherence estimates for those measures that require at least one dispensing event to be calculated. Measurement strategies that require a second dispensing event evidence even greater upward bias. These biases are greatest with shorter observation times. Furthermore, requiring a dispensing to be calculated meant that these measures could not be defined for large numbers of individuals (17-32 % of participants in this study). Measurement strategies that do not require a dispensing event to be calculated appear least vulnerable to these biases and can be calculated for everyone. However they do require additional assumptions and data (e.g., pre-intervention dispensing data) to support their validity. CONCLUSIONS: Many adherence measures require one, or sometimes two, dispensings in order to be defined. Since such measures assume all dispensed medication is used as directed, they have a built in upward bias that is especially pronounced when they are calculated over relatively short timeframes (< 9 months). Less biased measurement strategies that do not require a dispensing event are available, but require additional data to support their validity. TRIAL REGISTRATION: The study was funded by grant R01HL83433 from the National Heart, Lung and Blood Institute (NHLBI) and is filed as study NCT00414817 in the clinicaltrials.gov database.