Comparative analysis of attack detection methods in Delay Tolerant Network

Delay Tolerant Network is a new kind of wireless network which includes Radio Frequency (RF) and acoustic (sonar) technologies. DTN developed for an interplanetary network where the speed of light is slow. DTN is derived from deep space communication. DTN is distinguished as long delay and intermittent connectivity. The Delay Tolerant Network is more vulnerable to different kinds of attacks like flooding attack, blackhole and greyhole attacks, due to limited connectivity. There is no end-to-end connectivity between source & destination in DTN. So that it uses a store, carry and forward mechanism to transfer the data from one node to another node. The Delay Tolerant Network was developed to solve technical problems in the end-to-end network. DTN is becoming more and more important because communication networks are ubiquitous today. It provides automotive communication solutions. DTN is a decentralized and self-managed system with unique network attributes; however, attributes such as high mobility nodes, network uplinks and downlinks, and separate routing can cause network vulnerabilities. These vulnerabilities include the host being compromised, which in turn will bring security risks, because the compromised host may destroy the routing protocol in the network. This article analyses the various types of attack detection methods

Comparative analysis of attack detection methods in Delay Tolerant Network

Delay Tolerant Network is a new kind of wireless network which includes Radio Frequency (RF) and acoustic (sonar) technologies. DTN developed for an interplanetary network where the speed of light is slow. DTN is derived from deep space communication. DTN is distinguished as long delay and intermittent connectivity. The Delay Tolerant Network is more vulnerable to different kinds of attacks like flooding attack, blackhole and greyhole attacks, due to limited connectivity. There is no end-to-end connectivity between source & destination in DTN. So that it uses a store, carry and forward mechanism to transfer the data from one node to another node. The Delay Tolerant Network was developed to solve technical problems in the end-to-end network. DTN is becoming more and more important because communication networks are ubiquitous today. It provides automotive communication solutions. DTN is a decentralized and self-managed system with unique network attributes; however, attributes such as high mobility nodes, network uplinks and downlinks, and separate routing can cause network vulnerabilities. These vulnerabilities include the host being compromised, which in turn will bring security risks, because the compromised host may destroy the routing protocol in the network. This article analyses the various types of attack detection methods

Providing accurate course/video recommendations in E-Learning environment using association rule mining and collaborative filtering

Internet has huge number of learning resources, reason why students fail to take benefit of those is because they don't know where to look for resources, and more importantly which of these will be ideal for their respective academics. To provide related content to students most of the institutes uses different E-Learning solutions which acts as a repository of learning resources for students.These E-learning solutions often don't provide personalized recommendations to users. We propose an E-Learning solution which provides users with recommendations based on his/her preferences and content consumed by similar students, further more system we propose provide all the facilities like course sharing between two universities, online tests, analytics etc. in one software. Collaborative filtering and its modifications is one of the most commonly used recommendation algorithm. Collaborative Filtering find people with similar interests, analyze their behavior derived from their ratings, and recommend target user the same items. As online social networks are growing, users can now make friends, share thoughts, images etc. on the Internet and express different level of trust on their web friends. Recommendations generated by the trusted friends are more relevant than other users. This paper proposes a video recommendation system that generates recommendations from the collaboration of trusted friends of the target user and uses association rule mining to capture current trends of users in the network

Results from the centers for disease control and prevention's predict the 2013-2014 Influenza Season Challenge

Background: Early insights into the timing of the start, peak, and intensity of the influenza season could be useful in planning influenza prevention and control activities. To encourage development and innovation in influenza forecasting, the Centers for Disease Control and Prevention (CDC) organized a challenge to predict the 2013-14 Unites States influenza season. Methods: Challenge contestants were asked to forecast the start, peak, and intensity of the 2013-2014 influenza season at the national level and at any or all Health and Human Services (HHS) region level(s). The challenge ran from December 1, 2013-March 27, 2014; contestants were required to submit 9 biweekly forecasts at the national level to be eligible. The selection of the winner was based on expert evaluation of the methodology used to make the prediction and the accuracy of the prediction as judged against the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). Results: Nine teams submitted 13 forecasts for all required milestones. The first forecast was due on December 2, 2013; 3/13 forecasts received correctly predicted the start of the influenza season within one week, 1/13 predicted the peak within 1 week, 3/13 predicted the peak ILINet percentage within 1 %, and 4/13 predicted the season duration within 1 week. For the prediction due on December 19, 2013, the number of forecasts that correctly forecasted the peak week increased to 2/13, the peak percentage to 6/13, and the duration of the season to 6/13. As the season progressed, the forecasts became more stable and were closer to the season milestones. Conclusion: Forecasting has become technically feasible, but further efforts are needed to improve forecast accuracy so that policy makers can reliably use these predictions. CDC and challenge contestants plan to build upon the methods developed during this contest to improve the accuracy of influenza forecasts. © 2016 The Author(s)

Optimization and Stability Testing of Four Commercially Available Dried Blood Spot Devices for Estimating Measles and Rubella IgG Antibodies.

Blood collection using dried blood spots (DBS) provides an easier alternative to venipuncture for sample collection, transport, and storage but requires additional processing that can cause variability in results. Whole-blood samples spotted on four DBS devices and respective paired serum samples were tested for antimeasles and antirubella IgG antibody concentrations by enzyme immunoassay. Elution protocols for DBS devices were optimized for comparability relative to serum samples using 12 adult volunteers. Stability of DBS collected on HemaSpot HF was assessed under various temperature conditions (+4, 22 to 25, and 45°C) at six time points (0, 7, 15, 30, 60, and 90 days) in a controlled laboratory setting using six adult volunteers. Devices were shipped and stored for 30 days at four settings with variable temperature and humidity conditions to assess the impact on antibody concentrations. Three DBS devices demonstrated comparable antibody concentrations with paired sera following optimization. Antibodies recovered from DBS were stable for at least 90 days at 4°C and for 30 days at ambient temperature (22 to 25°C) using the HemaSpot HF device. A drastic decline in antibody concentrations was observed at 45°C, resulting in quantitative and qualitative discrepancies by day 7. HemaSpot HF devices shipped to field sites and stored at ambient temperature and humidity resulted in quantitative, but not qualitative, variability. Measurement of antimeasles and antirubella IgG antibodies with DBS devices is an accurate alternative to testing serum, provided elution protocols are optimized. Stability of HemaSpot HF devices at ambient temperature enables broader use in surveys when serum processing and cold storage are not feasible. IMPORTANCE Dried blood spot (DBS) collection offers various advantages over conventional methods of blood collection, especially when collecting and transporting samples for a serosurvey. Yet use of DBS requires additional processing steps in the laboratory that can add to variability in results. We optimized a protocol to elute IgG antibodies against measles and rubella viruses in four DBS devices, demonstrating high concordance with paired venous sera for most devices. Extensive stability studies with various temperature and storage conditions in the laboratory and in the field were conducted using HemaSpot HF DBS devices prior to its use in one of the largest community-based measles and rubella serological surveys in the world

Implementing Serosurveys in India: Experiences, Lessons Learned, and Recommendations.

Serological surveillance for vaccine-preventable diseases, such as measles and rubella, can provide direct measures of population immunity across age groups, identify gaps in immunity, and document changes in immunity over time. Rigorously conducted, representative household serosurveys provide high-quality estimates with minimal bias. However, they can be logistically challenging, expensive, and have higher refusal rates than vaccine coverage surveys. This article shares lessons learned through implementing nine measles and rubella household serosurveys in five districts in India-the challenges faced, the potential impact on results, and recommendations to facilitate the conduct of serosurveys. Specific lessons learned arose from challenges related to community mobilization owing to lack of cooperation in certain settings and populations, limitations of outdated census information, nonresponse due to refusal or unavailability during survey enumeration and enrollment, data collection issues, and specimen collection and handling issues. Although some experiences are specific to serosurveys in India, these lessons are generalizable to other household surveys, particularly vaccination coverage and serosurveys conducted in low- and middle-income settings

Diagnostic Accuracy of Dried Blood Spots Collected on HemaSpot HF Devices Compared to Venous Blood Specimens To Estimate Measles and Rubella Seroprevalence.

Fingerprick blood spotted onto filter paper offers an alternative to venous blood for use in population-based surveillance because it is comparatively inexpensive, acceptable, and easy to manage in the field. Prior studies have shown excellent agreement for immunoglobulin G (IgG) antibody detection from dried blood spots (DBS) and venous blood samples. However, much of this evidence is from high-income settings or laboratories where the samples were unlikely to be exposed to extreme temperatures and humidity, factors known to degrade DBS. We report the diagnostic accuracy of DBS collected using HemaSpot HF devices against venous sera in measuring measles- and rubella-specific IgG antibodies in a household serosurvey conducted in two districts in India. Paired serum and DBS samples collected by fingerprick were collected from women aged 15 to 50 years enrolled in a serosurvey in Palghar District of Maharashtra and Kanpur Nagar District of Uttar Pradesh in India. Specimen quality and volume were assessed in the laboratory. Samples were tested for antimeasles and antirubella IgG antibodies by an enzyme-linked immunosorbent assay (ELISA) (Euroimmun). Sensitivity of antibody detection by DBS was greater than 98%, and specificity was 90% and 98%, for measles and rubella IgG, respectively. Antibody concentrations were strongly correlated between paired specimens with adequate volume (measles R2 = 0.94; rubella R2 = 0.89). Although correlation was poor if DBS specimens had lower volumes, impact on qualitative results was minimal. This study showed DBS collected with HemaSpot HF devices can generate highly accurate results of measles- and rubella-specific IgG compared to sera in community-based surveys when protocols are optimized for DBS specimens. IMPORTANCE Dried blood spot (DBS) collection provides an easy, practical, and acceptable alternative to venous blood collection, especially for community-based studies, provided that results from DBS are accurate. We demonstrated high sensitivity and specificity for measles- and rubella-specific immunoglobulin G (IgG) with DBS collected via HemaSpot HF devices compared to serum samples. This is one of the largest community-based diagnostic accuracy studies of measles and rubella antibody testing with DBS and the first application we are aware of using HemaSpot HF device for measles and rubella serology. Results support the use of DBS in community-based serosurveillance

3 years of liraglutide versus placebo for type 2 diabetes risk reduction and weight management in individuals with prediabetes: a randomised, double-blind trial

Background: Liraglutide 3·0 mg was shown to reduce bodyweight and improve glucose metabolism after the 56-week period of this trial, one of four trials in the SCALE programme. In the 3-year assessment of the SCALE Obesity and Prediabetes trial we aimed to evaluate the proportion of individuals with prediabetes who were diagnosed with type 2 diabetes. Methods: In this randomised, double-blind, placebo-controlled trial, adults with prediabetes and a body-mass index of at least 30 kg/m2, or at least 27 kg/m2 with comorbidities, were randomised 2:1, using a telephone or web-based system, to once-daily subcutaneous liraglutide 3·0 mg or matched placebo, as an adjunct to a reduced-calorie diet and increased physical activity. Time to diabetes onset by 160 weeks was the primary outcome, evaluated in all randomised treated individuals with at least one post-baseline assessment. The trial was conducted at 191 clinical research sites in 27 countries and is registered with ClinicalTrials.gov, number NCT01272219. Findings: The study ran between June 1, 2011, and March 2, 2015. We randomly assigned 2254 patients to receive liraglutide (n=1505) or placebo (n=749). 1128 (50%) participants completed the study up to week 160, after withdrawal of 714 (47%) participants in the liraglutide group and 412 (55%) participants in the placebo group. By week 160, 26 (2%) of 1472 individuals in the liraglutide group versus 46 (6%) of 738 in the placebo group were diagnosed with diabetes while on treatment. The mean time from randomisation to diagnosis was 99 (SD 47) weeks for the 26 individuals in the liraglutide group versus 87 (47) weeks for the 46 individuals in the placebo group. Taking the different diagnosis frequencies between the treatment groups into account, the time to onset of diabetes over 160 weeks among all randomised individuals was 2·7 times longer with liraglutide than with placebo (95% CI 1·9 to 3·9, p<0·0001), corresponding with a hazard ratio of 0·21 (95% CI 0·13–0·34). Liraglutide induced greater weight loss than placebo at week 160 (–6·1 [SD 7·3] vs −1·9% [6·3]; estimated treatment difference −4·3%, 95% CI −4·9 to −3·7, p<0·0001). Serious adverse events were reported by 227 (15%) of 1501 randomised treated individuals in the liraglutide group versus 96 (13%) of 747 individuals in the placebo group. Interpretation: In this trial, we provide results for 3 years of treatment, with the limitation that withdrawn individuals were not followed up after discontinuation. Liraglutide 3·0 mg might provide health benefits in terms of reduced risk of diabetes in individuals with obesity and prediabetes. Funding: Novo Nordisk, Denmark

Risk profiles and one-year outcomes of patients with newly diagnosed atrial fibrillation in India: Insights from the GARFIELD-AF Registry.

BACKGROUND: The Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF) is an ongoing prospective noninterventional registry, which is providing important information on the baseline characteristics, treatment patterns, and 1-year outcomes in patients with newly diagnosed non-valvular atrial fibrillation (NVAF). This report describes data from Indian patients recruited in this registry. METHODS AND RESULTS: A total of 52,014 patients with newly diagnosed AF were enrolled globally; of these, 1388 patients were recruited from 26 sites within India (2012-2016). In India, the mean age was 65.8 years at diagnosis of NVAF. Hypertension was the most prevalent risk factor for AF, present in 68.5% of patients from India and in 76.3% of patients globally (P < 0.001). Diabetes and coronary artery disease (CAD) were prevalent in 36.2% and 28.1% of patients as compared with global prevalence of 22.2% and 21.6%, respectively (P < 0.001 for both). Antiplatelet therapy was the most common antithrombotic treatment in India. With increasing stroke risk, however, patients were more likely to receive oral anticoagulant therapy [mainly vitamin K antagonist (VKA)], but average international normalized ratio (INR) was lower among Indian patients [median INR value 1.6 (interquartile range {IQR}: 1.3-2.3) versus 2.3 (IQR 1.8-2.8) (P < 0.001)]. Compared with other countries, patients from India had markedly higher rates of all-cause mortality [7.68 per 100 person-years (95% confidence interval 6.32-9.35) vs 4.34 (4.16-4.53), P < 0.0001], while rates of stroke/systemic embolism and major bleeding were lower after 1 year of follow-up. CONCLUSION: Compared to previously published registries from India, the GARFIELD-AF registry describes clinical profiles and outcomes in Indian patients with AF of a different etiology. The registry data show that compared to the rest of the world, Indian AF patients are younger in age and have more diabetes and CAD. Patients with a higher stroke risk are more likely to receive anticoagulation therapy with VKA but are underdosed compared with the global average in the GARFIELD-AF. CLINICAL TRIAL REGISTRATION-URL: http://www.clinicaltrials.gov. Unique identifier: NCT01090362