Towards Developing a Robust Intrusion Detection Model Using Hadoop-Spark and Data Augmentation for IoT Networks.

In recent years, anomaly detection and machine learning for intrusion detection systems have been used to detect anomalies on Internet of Things networks. These systems rely on machine and deep learning to improve the detection accuracy. However, the robustness of the model depends on the number of datasamples available, quality of the data, and the distribution of the data classes. In the present paper, we focused specifically on the amount of data and class imbalanced since both parameters are key in IoT due to the fact that network traffic is increasing exponentially. For this reason, we propose a framework that uses a big data methodology with Hadoop-Spark to train and test multi-class and binary classification with one-vs-rest strategy for intrusion detection using the entire BoT IoT dataset. Thus, we evaluate all the algorithms available in Hadoop-Spark in terms of accuracy and processing time. In addition, since the BoT IoT dataset used is highly imbalanced, we also improve the accuracy for detecting minority classes by generating more datasamples using a Conditional Tabular Generative Adversarial Network (CTGAN). In general, our proposed model outperforms other published models including our previous model. Using our proposed methodology, the F1-score of one of the minority class, i.e., Theft attack was improved from 42% to 99%.

The effect of intrapartum antibiotics on early-onset neonatal sepsis in Dhaka, Bangladesh: a propensity score matched analysis.

BACKGROUND: We estimate the effect of antibiotics given in the intrapartum period on early-onset neonatal sepsis in Dhaka, Bangladesh using propensity score techniques. METHODS: We followed 600 mother-newborn pairs as part of a cohort study at a maternity center in Dhaka. Some pregnant women received one dose of intravenous antibiotics during labor based on clinician discretion. Newborns were followed over the first seven days of life for early-onset neonatal sepsis defined by a modified version of the World Health Organization Young Infants Integrated Management of Childhood Illnesses criteria.Using propensity scores we matched women who received antibiotics with similar women who did not. A final logistic regression model predicting sepsis was run in the matched sample controlling for additional potential confounders. RESULTS: Of the 600 mother-newborn pairs, 48 mothers (8.0%) received antibiotics during the intrapartum period. Seventy-seven newborns (12.8%) were classified with early-onset neonatal sepsis. Antibiotics appeared to be protective (odds ratio 0.381, 95% confidence interval 0.115-1.258), however this was not statistically significant. The results were similar after adjusting for prematurity, wealth status, and maternal colonization status (odds ratio 0.361, 95% confidence interval 0.106-1.225). CONCLUSIONS: Antibiotics administered during the intrapartum period may reduce the risk of early-onset neonatal sepsis in high neonatal mortality settings like Dhaka.

Evaluation of antinociceptive and antihyperglycemic activities in methanol extracts of whole plants of Alternanthera philoxeroides (Mart.) Griseb. (Amaranthaceae) in mice.

The present study evaluated the antinociceptive and antihyperglycemic effects of crude methanol extract of whole plants of Alternanthera philoxeroides (Mart.) Griseb. (Amaranthaceae) in Swiss albino mice. Antin(o)Ciceptive activity was evaluated by attenuation of the number of constrictions in acetic acid-induced gastric pain, while antihyperglycemic activity was evaluated through oral glucose tolerance tests in glucose-loaded mice. Dose-dependent and significant inhibitions in the number of constrictions were seen in mice administered with extract at doses of 50, 100, 200 and 400 mg per kg body weight. At these concentrations, the numbers of constrictions were reduced, respectively, by 31.0, 32.7, 37.9 and 44.8%. In comparison, a standard antinociceptive drug, aspirin reduced the number of constrictions by 37.9 and 67.2%, when administered at doses, respectively, of 200 and 400 mg per kg body weight. The extract also exhibited dose-dependent and significant antihyperglycemic activity when administered to mice at the afore-mentioned four doses. Serum glucose concentrations were reduced, respectively, by 36.3, 58.6, 65.0 and 65.6% at the four doses administered. The results compare favorably with a standard antihyperglycemic drug, glibenclamide, which when administered at a dose of 10 mg per kg body weight reduced serum glucose level by 42.7%. Taken together, the results obtained indicate that the extract merit further scientific studies towards discovery of components, which may prove beneficial in ameliorating pain, as well as high sugar levels of diabetic patients.