Flooding attacks to internet threat monitors (ITM): Modeling and counter measures using botnet and honeypots

The Internet Threat Monitoring (ITM),is a globally scoped Internet monitoring system whose goal is to measure, detect, characterize, and track threats such as distribute denial of service(DDoS) attacks and worms. To block the monitoring system in the internet the attackers are targeted the ITM system. In this paper we address flooding attack against ITM system in which the attacker attempt to exhaust the network and ITM's resources, such as network bandwidth, computing power, or operating system data structures by sending the malicious traffic. We propose an information-theoretic frame work that models the flooding attacks using Botnet on ITM. Based on this model we generalize the flooding attacks and propose an effective attack detection using Honeypots

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Efficacy of silver diamine fluoride as an antibacterial as well as antiplaque agent compared to fluoride varnish and acidulated phosphate fluoride gel: An in vivo study

Background: Silver diamine fluoride (SDF) is already proven as an antibacterial agent in vitro. Present study was formulated to compare the efficacy of SDF as an antibacterial as well as antiplaque agent in vivo with fluoride varnish and acidulated phosphate fluoride (APF) gel. Study Design: Total 123 children (male = 82, female = 41) were included in the study for a period of 18 months. Children were divided into three different groups-Group 1: SDF; Group 2: fluoride varnish; and Group 3: APF gel. All subjects were evaluated via plaque score at 6 th , 12 th , and 18 th months as well as Streptococcus mutans counts in saliva at 72 h, 6 th , 12 th , and 18 th months of follow-up. Results: Significant reduction was found in plaque score as well as S. mutans counts irrespective of group division. On intergroup comparison, no statistically significant difference was found in plaque score, but significant reduction in S. mutans counts was found in Group 1 as compared with Groups 2 and 3, while no significant difference was found between Groups 2 and 3. Conclusion: In vivo application of SDF on enamel significantly decreases S. mutans counts as compared to fluoride varnish and APF gel

Concepts of occlusion in prosthodontics: A literature review, part II

This series of articles describes about concepts of occlusion in the complete denture, fixed partial denture, and implants. This article discusses about the evolution of different concepts of nonbalanced occlusion and occlusal schemes in complete denture occlusion

Concepts of occlusion in prosthodontics: A literature review, part I

Occlusion and its relationship to the function of the stomatognathic system have been widely studied in dentistry since many decades. This series of articles describe about occlusion in the complete denture, fixed partial denture, and implants. Part I and II of this articles series describe concepts and philosophies of occlusion in complete denture. So far, available research has not concluded a superior tooth form or occlusal scheme to satisfy the requirements of completely edentulous patients with respect to comfort, mastication, phonetics, and esthetics. Since then, several balanced and nonbalanced articulation concepts were proposed in the literature. A balanced articulation appears to be most appropriate because of tooth contacts observed during nonfunctional activities of patients. This article discusses about evolution of different concepts of occlusion and occlusal schemes in complete denture occlusion

Segmentation Using the IC2T Model and Classification of Diabetic Retinopathy Using the Rock Hyrax Swarm-Based Coordination Attention Mechanism

Diabetic Retinopathy (DR) evaluations are increasingly being automated using artificial intelligence. Diabetes-related retinal vascular disease is a major cause of blindness and visual impairment worldwide. Therefore, automated DR detection devices would greatly aid in reducing visual impairment due to DR through early screening and treatment. Researchers have provided many techniques for picking out abnormalities in retinal images during the past several years. In the past, automated methods for diagnosing diabetic retinopathy required a human to extract information from retinal images before passing them on to a classifier. This study takes a novel two-pronged approach to automated DR classification to solve the issues. Due to the low positive instance percentage of existing asymmetric, we segment O.D.s and B.V.s with an enhanced version of an improved contoured convolutional transformer (IC2T). We develop a contoured optical disc (OD), a blood vessels (BV) detection module, and a dual convolutional transformer block that combines local and global contexts to make trustworthy associations. A second-stage Improved Coordination Attention Mechanism (ICAM) network is trained to recognize retinal biomarkers for DR such as microaneurysms (M.A.), haemorrhages (H.M.), and exudates (EX). With an average accuracy of 96%, 97%, and 98% on EyePACS-1, Messidor-2, and DIARETDB0, respectively, the suggested technique has proven itself to be at the field’s cutting edge. Comprehensive testing and comparisons to established methods support the proposed strategy

Policy assistance for adoption of residential solar PV in India: A stakeholder-centric approach for welfare optimization

This paper presents a comprehensive analysis of the consumer-centric business model for rooftop solar PV installations in India. We explore areas where potential policy interventions may be introduced to improve collective stakeholder benefits and incentivize more domestic consumers to adopt rooftop solar power generation in their premises. The proposed policy framework optimizes Feed-in Tariff (FiT) rates, PV capacities and Average Billing Rates (ABRs) towards maximizing stakeholder benefits. The stakeholders considered are the consumers/prosumers and the utility. Case studies with three residential prosumers of different demand and generation profiles are presented. The models for utility profit and prosumer savings are developed, and a multi-objective problem is formulated with FiT, generation capacity (as a function of demand) and ABR as decision variables. The pareto-optimal front is identified for prosumer and utility benefits and suitable points with reasonable tradeoffs are selected based on sensitivity analysis of the impact on collective welfare. The suitability of prevailing tariff and FiT rates of two Indian utilities namely, MSEDCL and TATA POWER, Delhi are studied, and their impact on prosumer savings and utility profits is brought out. The workflow to fix tariff, FiT and local PV capacities in active residential distribution systems is devised, providing the policymakers an effective decision-making tool