Cloud Forensics Investigation: Tracing Infringing Sharing of Copyrighted Content in Cloud

Cloud Computing is becoming a significant technology trend nowadays, but its abrupt rise also creates a brand new front for cybercrime investigation with various challenges. One of the challenges is to track down infringing sharing of copyrighted content in cloud. To solve this problem, we study a typical type of content sharing technologies in cloud computing, analyze the challenges that the new technologies bring to forensics, formalize a procedure to get digital evidences and obtain analytical results based on the evidences to track down illegal uploader. Furthermore, we propose a reasoning model based on the probability distribution in a Bayesian Network to evaluate the analytical result of forensics examinations. The proposed method can accurately and scientifically track down the origin infringing content uploader and owner. Keywords: cloud forensics, peer to peer, file sharing, tracking, CloudFron

Mucormycosis: an emerging disease?

ABSTRACTMucormycosis is the third invasive mycosis in order of importance after candidiasis and aspergillosis and is caused by fungi of the class Zygomycetes. The most important species in order of frequency is Rhizopus arrhizus (oryzae). Identification of the agents responsible for mucormycosis is based on macroscopic and microscopic morphological criteria, carbohydrate assimilation and the maximum temperature compatible with its growth. The incidence of mucormycosis is approximately 1.7 cases per 1000 000 inhabitants per year, and the main risk-factors for the development of mucormycosis are ketoacidosis (diabetic or other), iatrogenic immunosuppression, use of corticosteroids or deferoxamine, disruption of mucocutaneous barriers by catheters and other devices, and exposure to bandages contaminated by these fungi. Mucorales invade deep tissues via inhalation of airborne spores, percutaneous inoculation or ingestion. They colonise a high number of patients but do not cause invasion. Mucormycosis most commonly manifests in the sinuses (39%), lungs (24%), skin (19%), brain (9%), and gastrointestinal tract (7%), in the form of disseminated disease (6%), and in other sites (6%). Clinical diagnosis of mucormycosis is difficult, and is often made at a late stage of the disease or post-mortem. Confirmation of the clinical form requires the combination of symptoms compatible with histological invasion of tissues. The probable diagnosis of mucormycosis requires the combination of various clinical data and the isolation in culture of the fungus from clinical samples. Treatment of mucormycosis requires a rapid diagnosis, correction of predisposing factors, surgical resection, debridement and appropriate antifungal therapy. Liposomal amphotericin B is the therapy of choice for this condition. Itraconazole is considered to be inappropriate and there is evidence of its failure in patients suffering from mucormycosis. Voriconazole is not active in vitro against Mucorales, and failed when used in vivo. Posaconazole and ravuconazole have good activity in vitro. The overall rate of mortality of mucormycosis is approximately 40%

Multi-Parameter Sensitivity Analysis of a Bayesian Network from a Digital Forensic Investigation

A multi-parameter sensitivity analysis of a Bayesian network (BN) used in the digital forensic investigation of the Yahoo! email case has been performed using the principle of ‘steepest gradient’ in the parameter space of the conditional probabilities. This procedure delivers a more reliable result for the dependence of the posterior probability of the BN on the values used to populate the conditional probability tables (CPTs) of the BN. As such, this work extends our previous studies using singleparameter sensitivity analyses of BNs, with the overall aim of more deeply understanding the indicative use of BNs within the digital forensic and prosecutorial processes. In particular, we find that while our previous conclusions regarding the Yahoo! email case are generally validated by the results of the multi-parameter sensitivity analysis, the utility of performing the latter analysis as a means of verifying the structure and form adopted for the Bayesian network should not be underestimated. Keywords: Bayesian network; digital forensics; multi-parameter sensitivity analysis; steepest gradient

Opportunities for evaluating chemical exposures and child health in the United States: the Environmental influences on Child Health Outcomes (ECHO) Program.

The Environmental Influences on Child Health Outcomes (ECHO) Program will evaluate environmental factors affecting children's health (perinatal, neurodevelopmental, obesity, respiratory, and positive health outcomes) by pooling cohorts composed of >50,000 children in the largest US study of its kind. Our objective was to identify opportunities for studying chemicals and child health using existing or future ECHO chemical exposure data. We described chemical-related information collected by ECHO cohorts and reviewed ECHO-relevant literature on exposure routes, sources, and environmental and human monitoring. Fifty-six ECHO cohorts have existing or planned chemical biomonitoring data for mothers or children. Environmental phenols/parabens, phthalates, metals/metalloids, and tobacco biomarkers are each being measured by ≥15 cohorts, predominantly during pregnancy and childhood, indicating ample opportunities to study child health outcomes. Cohorts are collecting questionnaire data on multiple exposure sources and conducting environmental monitoring including air, dust, and water sample collection that could be used for exposure assessment studies. To supplement existing chemical data, we recommend biomonitoring of emerging chemicals, nontargeted analysis to identify novel chemicals, and expanded measurement of chemicals in alternative biological matrices and dust samples. ECHO's rich data and samples represent an unprecedented opportunity to accelerate environmental chemical research to improve the health of US children