Denial of Service Detection for IoT Networks Using Machine Learning

The Internet of Things (IoT) is considered one of the trending technologies today. IoT affects a variety of industries, including logistics tracking, healthcare, automotive and smart cities. A rising number of cyberattacks and breaches are rapidly targeting networks equipped with IoT devices. Due to the resource-constrained nature of the IoT devices, one of the Internet security issues impacting IoT devices is the Denial-of-Service (DoS). This encourages the development of new techniques for automatically detecting DoS in IoT networks. In this paper, we test the performance of the following Machine Learning (ML) algorithms in detecting IoT DoS attacks using packet analysis at regular time intervals: Neural Networks (NN), Gaussian Naive Bayes (NB), Decision Trees (DT), and Support Vector Machine (SVM). We were able to achieve 98% accuracy in intrusion detection for IoT devices. We have created a novel way of detecting the attacks using only six attributes, which significantly reduces the time to train the ML Models by 58% on average. This research is based on data collected from actual IoT attacks on IoT networks. This paper shows that using the DT or NN; we can detect attacks on IoT devices. Furthermore, it shows that NB and SVM are poor in detecting IoT attacks. In addition, it proves that middle boxes embedded with ML Models can be utilized to detect attacks in places such as houses, manufactures, and plants

History Shaped the Geographic Distribution of Genomic Admixture on the Island of Puerto Rico

Contemporary genetic variation among Latin Americans human groups reflects population migrations shaped by complex historical, social and economic factors. Consequently, admixture patterns may vary by geographic regions ranging from countries to neighborhoods. We examined the geographic variation of admixture across the island of Puerto Rico and the degree to which it could be explained by historic and social events. We analyzed a census-based sample of 642 Puerto Rican individuals that were genotyped for 93 ancestry informative markers (AIMs) to estimate African, European and Native American ancestry. Socioeconomic status (SES) data and geographic location were obtained for each individual. There was significant geographic variation of ancestry across the island. In particular, African ancestry demonstrated a decreasing East to West gradient that was partially explained by historical factors linked to the colonial sugar plantation system. SES also demonstrated a parallel decreasing cline from East to West. However, at a local level, SES and African ancestry were negatively correlated. European ancestry was strongly negatively correlated with African ancestry and therefore showed patterns complementary to African ancestry. By contrast, Native American ancestry showed little variation across the island and across individuals and appears to have played little social role historically. The observed geographic distributions of SES and genetic variation relate to historical social events and mating patterns, and have substantial implications for the design of studies in the recently admixed Puerto Rican population. More generally, our results demonstrate the importance of incorporating social and geographic data with genetics when studying contemporary admixed populations

Mapping human genetic diversity in Asia

Asia harbors substantial cultural and linguistic diversity, but the geographic structure of genetic variation across the continent remains enigmatic. Here we report a large-scale survey of autosomal variation from a broad geographic sample of Asian human populations. Our results show that genetic ancestry is strongly correlated with linguistic affiliations as well as geography. Most populations show relatedness within ethnic/linguistic groups, despite prevalent gene flow among populations. More than 90% of East Asian (EA) haplotypes could be found in either Southeast Asian (SEA) or Central-South Asian (CSA) populations and show clinal structure with haplotype diversity decreasing from south to north. Furthermore, 50% of EA haplotypes were found in SEA only and 5% were found in CSA only, indicating that SEA was a major geographic source of EA populations

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Not AvailableThe disease outbreaks in aquaculture system of wetlands are the major cause of fish mortality. Among various bacterial septicaemic diseases, fish mortality caused by Acinetobacter spp. is recently reported in different fish species. Fish disease outbreak was investigated in a wetland of West Bengal, India to identify the aetiological factors involved. The moribund fish were examined and subjected to bacterial isolation. Two bacterial causative agents were identified as Acinetobacter junii and Acinetobacter pittii by biochemical characterization and 16S rRNA gene amplification. Both the isolates were oxidase-negative, nitrate-negative, catalase-positive and indole-negative. The molecular identification using 16S rRNA gene sequencing and phylogenetic tree analysis further confirmed the two Acinetobacter spp. with 97%–99% similarity. The antibiotic resistance patterns of these two bacteria revealed that both of them were resistant to β-lactam, cefalexin, cephalothin, amoxyclav, cefuroxime, cefadroxil, clindamycin, vancomycin and penicillin. In addition, A. pittii was also resistant to other antibiotics of cephams group such as ceftazidime and cefotaxime. In the challenge experiment, both A. junii and A. pittii were found to be pathogenic with LD50 of 1.24 × 105 and 1.88 × 107 cfu/fish respectively. Histopathological examination of gill, liver and kidney revealed prominent changes supporting bacterial septicaemia. The investigation reports for the first time on concurrent infection by A. junii and multidrug-resistant (MDR)-A. pittii as emerging fish pathogens to cause severe mortality in Labeo catla and Hypophthalmichthys molitrix in a freshwater wetland.Not Availabl

Secreted Proteins from the Helminth Fasciola hepatica Inhibit the Initiation of Autoreactive T Cell Responses and Prevent Diabetes in the NOD Mouse

Infections with helminth parasites prevent/attenuate auto-inflammatory disease. Here we show that molecules secreted by a helminth parasite could prevent Type 1 Diabetes (T1D) in nonobese diabetic (NOD) mice. When delivered at 4 weeks of age (coincident with the initiation of autoimmunity), the excretory/secretory products of Fasciola hepatica (FhES) prevented the onset of T1D, with 84% of mice remaining normoglycaemic and insulitis-free at 30 weeks of age. Disease protection was associated with suppression of IFN-γ secretion from autoreactive T cells and a switch to the production of a regulatory isotype (from IgG2a to IgG1) of autoantibody. Following FhES injection, peritoneal macrophages converted to a regulatory M2 phenotype, characterised by increased expression levels of Ym1, Arg-1, TGFb and PD-L1. Expression of these M2 genetic markers increased in the pancreatic lymph nodes and the pancreas of FhES-treated mice. In vitro , FhES-stimulated M2 macrophages induced the differentiation of Tregs from splenocytes isolated from naïve NOD mice. Collectively, our data shows that FhES contains immune-modulatory molecules that mediate protection from autoimmune diabetes via the induction and maintenance of a regulatory immune environment. © 2014 Lund et al